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HBR’s Most-Read Research Articles of 2022

  • Dagny Dukach

research paper of 2022

Insights on equity, leadership, and becoming your best self.

The new year is a great time to set ambitious goals. But alongside our plans for the future, it’s also helpful to acknowledge all the challenges we’ve faced — and the progress we’ve made — in the last 12 months. In this end-of-year roundup, we share key insights and trends from HBR’s most-read research articles of 2022, exploring topics from embracing a new identity to fostering equity in the workplace and beyond.

For many of us, the arrival of a new year can be equal parts inspiring and daunting. While the promise of a fresh start is often welcome, it’s also a reminder of all the challenges we faced in the last 12 months — and all those still awaiting us, that we have yet to overcome.

research paper of 2022

  • Dagny Dukach is a former associate editor at Harvard Business Review.

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research paper of 2022

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Journal of Machine Learning Research

The Journal of Machine Learning Research (JMLR), established in 2000 , provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online.

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Latest papers

Optimal Locally Private Nonparametric Classification with Public Data Yuheng Ma, Hanfang Yang , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Learning to Warm-Start Fixed-Point Optimization Algorithms Rajiv Sambharya, Georgina Hall, Brandon Amos, Bartolomeo Stellato , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks Yunfei Yang, Ding-Xuan Zhou , 2024. [ abs ][ pdf ][ bib ]

Nonparametric Copula Models for Multivariate, Mixed, and Missing Data Joseph Feldman, Daniel R. Kowal , 2024. [ abs ][ pdf ][ bib ]      [ code ]

An Analysis of Quantile Temporal-Difference Learning Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney , 2024. [ abs ][ pdf ][ bib ]

Conformal Inference for Online Prediction with Arbitrary Distribution Shifts Isaac Gibbs, Emmanuel J. Candès , 2024. [ abs ][ pdf ][ bib ]      [ code ]

More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization Xu Liu, Heng Lian, Jian Huang , 2024. [ abs ][ pdf ][ bib ]

A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment Robert Hu, Dino Sejdinovic, Robin J. Evans , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Assessing the Overall and Partial Causal Well-Specification of Nonlinear Additive Noise Models Christoph Schultheiss, Peter Bühlmann , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Simple Cycle Reservoirs are Universal Boyu Li, Robert Simon Fong, Peter Tino , 2024. [ abs ][ pdf ][ bib ]

On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling Rong Tang, Yun Yang , 2024. [ abs ][ pdf ][ bib ]

Generalization and Stability of Interpolating Neural Networks with Minimal Width Hossein Taheri, Christos Thrampoulidis , 2024. [ abs ][ pdf ][ bib ]

Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression Jiading Liu, Lei Shi , 2024. [ abs ][ pdf ][ bib ]

Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng, Tongliang Liu, Kun Zhang, Dacheng Tao , 2024. [ abs ][ pdf ][ bib ]

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Kernel Thinning Raaz Dwivedi, Lester Mackey , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions Xuxing Chen, Tesi Xiao, Krishnakumar Balasubramanian , 2024. [ abs ][ pdf ][ bib ]

Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks Yunpeng Zhao, Ning Hao, Ji Zhu , 2024. [ abs ][ pdf ][ bib ]

Statistical Inference for Fairness Auditing John J. Cherian, Emmanuel J. Candès , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning Yiling Xie, Xiaoming Huo , 2024. [ abs ][ pdf ][ bib ]

DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing , 2024. (Machine Learning Open Source Software Paper) [ abs ][ pdf ][ bib ]      [ code ]

Flexible Bayesian Product Mixture Models for Vector Autoregressions Suprateek Kundu, Joshua Lukemire , 2024. [ abs ][ pdf ][ bib ]

A Variational Approach to Bayesian Phylogenetic Inference Cheng Zhang, Frederick A. Matsen IV , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Fat-Shattering Dimension of k-fold Aggregations Idan Attias, Aryeh Kontorovich , 2024. [ abs ][ pdf ][ bib ]

Unified Binary and Multiclass Margin-Based Classification Yutong Wang, Clayton Scott , 2024. [ abs ][ pdf ][ bib ]

Neural Feature Learning in Function Space Xiangxiang Xu, Lizhong Zheng , 2024. [ abs ][ pdf ][ bib ]      [ code ]

PyGOD: A Python Library for Graph Outlier Detection Kay Liu, Yingtong Dou, Xueying Ding, Xiyang Hu, Ruitong Zhang, Hao Peng, Lichao Sun, Philip S. Yu , 2024. (Machine Learning Open Source Software Paper) [ abs ][ pdf ][ bib ]      [ code ]

Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria Tengyuan Liang , 2024. [ abs ][ pdf ][ bib ]

Fixed points of nonnegative neural networks Tomasz J. Piotrowski, Renato L. G. Cavalcante, Mateusz Gabor , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks Fanghui Liu, Leello Dadi, Volkan Cevher , 2024. [ abs ][ pdf ][ bib ]

A Survey on Multi-player Bandits Etienne Boursier, Vianney Perchet , 2024. [ abs ][ pdf ][ bib ]

Transport-based Counterfactual Models Lucas De Lara, Alberto González-Sanz, Nicholas Asher, Laurent Risser, Jean-Michel Loubes , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Adaptive Latent Feature Sharing for Piecewise Linear Dimensionality Reduction Adam Farooq, Yordan P. Raykov, Petar Raykov, Max A. Little , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Topological Node2vec: Enhanced Graph Embedding via Persistent Homology Yasuaki Hiraoka, Yusuke Imoto, Théo Lacombe, Killian Meehan, Toshiaki Yachimura , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length Katerina Hlaváčková-Schindler, Anna Melnykova, Irene Tubikanec , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Representation Learning via Manifold Flattening and Reconstruction Michael Psenka, Druv Pai, Vishal Raman, Shankar Sastry, Yi Ma , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Bagging Provides Assumption-free Stability Jake A. Soloff, Rina Foygel Barber, Rebecca Willett , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Fairness guarantees in multi-class classification with demographic parity Christophe Denis, Romuald Elie, Mohamed Hebiri, François Hu , 2024. [ abs ][ pdf ][ bib ]

Regimes of No Gain in Multi-class Active Learning Gan Yuan, Yunfan Zhao, Samory Kpotufe , 2024. [ abs ][ pdf ][ bib ]

Learning Optimal Dynamic Treatment Regimens Subject to Stagewise Risk Controls Mochuan Liu, Yuanjia Wang, Haoda Fu, Donglin Zeng , 2024. [ abs ][ pdf ][ bib ]

Margin-Based Active Learning of Classifiers Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice , 2024. [ abs ][ pdf ][ bib ]

Random Subgraph Detection Using Queries Wasim Huleihel, Arya Mazumdar, Soumyabrata Pal , 2024. [ abs ][ pdf ][ bib ]

Classification with Deep Neural Networks and Logistic Loss Zihan Zhang, Lei Shi, Ding-Xuan Zhou , 2024. [ abs ][ pdf ][ bib ]

Spectral learning of multivariate extremes Marco Avella Medina, Richard A Davis, Gennady Samorodnitsky , 2024. [ abs ][ pdf ][ bib ]

Sum-of-norms clustering does not separate nearby balls Alexander Dunlap, Jean-Christophe Mourrat , 2024. [ abs ][ pdf ][ bib ]      [ code ]

An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization Guy Kornowski, Ohad Shamir , 2024. [ abs ][ pdf ][ bib ]

Linear Distance Metric Learning with Noisy Labels Meysam Alishahi, Anna Little, Jeff M. Phillips , 2024. [ abs ][ pdf ][ bib ]      [ code ]

OpenBox: A Python Toolkit for Generalized Black-box Optimization Huaijun Jiang, Yu Shen, Yang Li, Beicheng Xu, Sixian Du, Wentao Zhang, Ce Zhang, Bin Cui , 2024. (Machine Learning Open Source Software Paper) [ abs ][ pdf ][ bib ]      [ code ]

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Predictive Inference with Weak Supervision Maxime Cauchois, Suyash Gupta, Alnur Ali, John C. Duchi , 2024. [ abs ][ pdf ][ bib ]

Functions with average smoothness: structure, algorithms, and learning Yair Ashlagi, Lee-Ad Gottlieb, Aryeh Kontorovich , 2024. [ abs ][ pdf ][ bib ]

Differentially Private Data Release for Mixed-type Data via Latent Factor Models Yanqing Zhang, Qi Xu, Niansheng Tang, Annie Qu , 2024. [ abs ][ pdf ][ bib ]

The Non-Overlapping Statistical Approximation to Overlapping Group Lasso Mingyu Qi, Tianxi Li , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Faster Rates of Differentially Private Stochastic Convex Optimization Jinyan Su, Lijie Hu, Di Wang , 2024. [ abs ][ pdf ][ bib ]

Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization O. Deniz Akyildiz, Sotirios Sabanis , 2024. [ abs ][ pdf ][ bib ]

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Stable Implementation of Probabilistic ODE Solvers Nicholas Krämer, Philipp Hennig , 2024. [ abs ][ pdf ][ bib ]

More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime validity Borja Rodríguez-Gálvez, Ragnar Thobaben, Mikael Skoglund , 2024. [ abs ][ pdf ][ bib ]

Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space Zhengdao Chen , 2024. [ abs ][ pdf ][ bib ]

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Random Forest Weighted Local Fréchet Regression with Random Objects Rui Qiu, Zhou Yu, Ruoqing Zhu , 2024. [ abs ][ pdf ][ bib ]      [ code ]

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Unsupervised Anomaly Detection Algorithms on Real-world Data: How Many Do We Need? Roel Bouman, Zaharah Bukhsh, Tom Heskes , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini , 2024. [ abs ][ pdf ][ bib ]

Information Processing Equalities and the Information–Risk Bridge Robert C. Williamson, Zac Cranko , 2024. [ abs ][ pdf ][ bib ]

Nonparametric Regression for 3D Point Cloud Learning Xinyi Li, Shan Yu, Yueying Wang, Guannan Wang, Li Wang, Ming-Jun Lai , 2024. [ abs ][ pdf ][ bib ]      [ code ]

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Semi-supervised Inference for Block-wise Missing Data without Imputation Shanshan Song, Yuanyuan Lin, Yong Zhou , 2024. [ abs ][ pdf ][ bib ]

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MAP- and MLE-Based Teaching Hans Ulrich Simon, Jan Arne Telle , 2024. [ abs ][ pdf ][ bib ]

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Overparametrized Multi-layer Neural Networks: Uniform Concentration of Neural Tangent Kernel and Convergence of Stochastic Gradient Descent Jiaming Xu, Hanjing Zhu , 2024. [ abs ][ pdf ][ bib ]

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ptwt - The PyTorch Wavelet Toolbox Moritz Wolter, Felix Blanke, Jochen Garcke, Charles Tapley Hoyt , 2024. (Machine Learning Open Source Software Paper) [ abs ][ pdf ][ bib ]      [ code ]

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Pareto Smoothed Importance Sampling Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Policy Gradient Methods in the Presence of Symmetries and State Abstractions Prakash Panangaden, Sahand Rezaei-Shoshtari, Rosie Zhao, David Meger, Doina Precup , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Scaling Instruction-Finetuned Language Models Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, Jason Wei , 2024. [ abs ][ pdf ][ bib ]

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Optimal First-Order Algorithms as a Function of Inequalities Chanwoo Park, Ernest K. Ryu , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Resource-Efficient Neural Networks for Embedded Systems Wolfgang Roth, Günther Schindler, Bernhard Klein, Robert Peharz, Sebastian Tschiatschek, Holger Fröning, Franz Pernkopf, Zoubin Ghahramani , 2024. [ abs ][ pdf ][ bib ]

Trained Transformers Learn Linear Models In-Context Ruiqi Zhang, Spencer Frei, Peter L. Bartlett , 2024. [ abs ][ pdf ][ bib ]

Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees Nachuan Xiao, Xiaoyin Hu, Xin Liu, Kim-Chuan Toh , 2024. [ abs ][ pdf ][ bib ]

Efficient Modality Selection in Multimodal Learning Yifei He, Runxiang Cheng, Gargi Balasubramaniam, Yao-Hung Hubert Tsai, Han Zhao , 2024. [ abs ][ pdf ][ bib ]

A Multilabel Classification Framework for Approximate Nearest Neighbor Search Ville Hyvönen, Elias Jääsaari, Teemu Roos , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization Lorenzo Pacchiardi, Rilwan A. Adewoyin, Peter Dueben, Ritabrata Dutta , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Multiple Descent in the Multiple Random Feature Model Xuran Meng, Jianfeng Yao, Yuan Cao , 2024. [ abs ][ pdf ][ bib ]

Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling Ye He, Tyler Farghly, Krishnakumar Balasubramanian, Murat A. Erdogdu , 2024. [ abs ][ pdf ][ bib ]

Invariant and Equivariant Reynolds Networks Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai , 2024. (Machine Learning Open Source Software Paper) [ abs ][ pdf ][ bib ]      [ code ]

Personalized PCA: Decoupling Shared and Unique Features Naichen Shi, Raed Al Kontar , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee George H. Chen , 2024. [ abs ][ pdf ][ bib ]      [ code ]

On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang, Mengdi Wang, Alec Koppel , 2024. [ abs ][ pdf ][ bib ]

Convergence for nonconvex ADMM, with applications to CT imaging Rina Foygel Barber, Emil Y. Sidky , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Distributed Gaussian Mean Estimation under Communication Constraints: Optimal Rates and Communication-Efficient Algorithms T. Tony Cai, Hongji Wei , 2024. [ abs ][ pdf ][ bib ]

Sparse NMF with Archetypal Regularization: Computational and Robustness Properties Kayhan Behdin, Rahul Mazumder , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Deep Network Approximation: Beyond ReLU to Diverse Activation Functions Shijun Zhang, Jianfeng Lu, Hongkai Zhao , 2024. [ abs ][ pdf ][ bib ]

Effect-Invariant Mechanisms for Policy Generalization Sorawit Saengkyongam, Niklas Pfister, Predrag Klasnja, Susan Murphy, Jonas Peters , 2024. [ abs ][ pdf ][ bib ]

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Heterogeneous-Agent Reinforcement Learning Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng, Siyi Hu, Jiaming Ji, Yaodong Yang , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Sample-efficient Adversarial Imitation Learning Dahuin Jung, Hyungyu Lee, Sungroh Yoon , 2024. [ abs ][ pdf ][ bib ]

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Optimal Bump Functions for Shallow ReLU networks: Weight Decay, Depth Separation, Curse of Dimensionality Stephan Wojtowytsch , 2024. [ abs ][ pdf ][ bib ]

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge , 2024. [ abs ][ pdf ][ bib ]      [ code ]

On Tail Decay Rate Estimation of Loss Function Distributions Etrit Haxholli, Marco Lorenzi , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, Wenjing Liao , 2024. [ abs ][ pdf ][ bib ]

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A systematic review and research perspective on recommender systems

  • Deepjyoti Roy   ORCID: orcid.org/0000-0002-8020-7145 1 &
  • Mala Dutta 1  

Journal of Big Data volume  9 , Article number:  59 ( 2022 ) Cite this article

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Recommender systems are efficient tools for filtering online information, which is widespread owing to the changing habits of computer users, personalization trends, and emerging access to the internet. Even though the recent recommender systems are eminent in giving precise recommendations, they suffer from various limitations and challenges like scalability, cold-start, sparsity, etc. Due to the existence of various techniques, the selection of techniques becomes a complex work while building application-focused recommender systems. In addition, each technique comes with its own set of features, advantages and disadvantages which raises even more questions, which should be addressed. This paper aims to undergo a systematic review on various recent contributions in the domain of recommender systems, focusing on diverse applications like books, movies, products, etc. Initially, the various applications of each recommender system are analysed. Then, the algorithmic analysis on various recommender systems is performed and a taxonomy is framed that accounts for various components required for developing an effective recommender system. In addition, the datasets gathered, simulation platform, and performance metrics focused on each contribution are evaluated and noted. Finally, this review provides a much-needed overview of the current state of research in this field and points out the existing gaps and challenges to help posterity in developing an efficient recommender system.

Introduction

The recent advancements in technology along with the prevalence of online services has offered more abilities for accessing a huge amount of online information in a faster manner. Users can post reviews, comments, and ratings for various types of services and products available online. However, the recent advancements in pervasive computing have resulted in an online data overload problem. This data overload complicates the process of finding relevant and useful content over the internet. The recent establishment of several procedures having lower computational requirements can however guide users to the relevant content in a much easy and fast manner. Because of this, the development of recommender systems has recently gained significant attention. In general, recommender systems act as information filtering tools, offering users suitable and personalized content or information. Recommender systems primarily aim to reduce the user’s effort and time required for searching relevant information over the internet.

Nowadays, recommender systems are being increasingly used for a large number of applications such as web [ 1 , 67 , 70 ], books [ 2 ], e-learning [ 4 , 16 , 61 ], tourism [ 5 , 8 , 78 ], movies [ 66 ], music [ 79 ], e-commerce, news, specialized research resources [ 65 ], television programs [ 72 , 81 ], etc. It is therefore important to build high-quality and exclusive recommender systems for providing personalized recommendations to the users in various applications. Despite the various advances in recommender systems, the present generation of recommender systems requires further improvements to provide more efficient recommendations applicable to a broader range of applications. More investigation of the existing latest works on recommender systems is required which focus on diverse applications.

There is hardly any review paper that has categorically synthesized and reviewed the literature of all the classification fields and application domains of recommender systems. The few existing literature reviews in the field cover just a fraction of the articles or focus only on selected aspects such as system evaluation. Thus, they do not provide an overview of the application field, algorithmic categorization, or identify the most promising approaches. Also, review papers often neglect to analyze the dataset description and the simulation platforms used. This paper aims to fulfil this significant gap by reviewing and comparing existing articles on recommender systems based on a defined classification framework, their algorithmic categorization, simulation platforms used, applications focused, their features and challenges, dataset description and system performance. Finally, we provide researchers and practitioners with insight into the most promising directions for further investigation in the field of recommender systems under various applications.

In essence, recommender systems deal with two entities—users and items, where each user gives a rating (or preference value) to an item (or product). User ratings are generally collected by using implicit or explicit methods. Implicit ratings are collected indirectly from the user through the user’s interaction with the items. Explicit ratings, on the other hand, are given directly by the user by picking a value on some finite scale of points or labelled interval values. For example, a website may obtain implicit ratings for different items based on clickstream data or from the amount of time a user spends on a webpage and so on. Most recommender systems gather user ratings through both explicit and implicit methods. These feedbacks or ratings provided by the user are arranged in a user-item matrix called the utility matrix as presented in Table 1 .

The utility matrix often contains many missing values. The problem of recommender systems is mainly focused on finding the values which are missing in the utility matrix. This task is often difficult as the initial matrix is usually very sparse because users generally tend to rate only a small number of items. It may also be noted that we are interested in only the high user ratings because only such items would be suggested back to the users. The efficiency of a recommender system greatly depends on the type of algorithm used and the nature of the data source—which may be contextual, textual, visual etc.

Types of recommender systems

Recommender systems are broadly categorized into three different types viz. content-based recommender systems, collaborative recommender systems and hybrid recommender systems. A diagrammatic representation of the different types of recommender systems is given in Fig.  1 .

figure 1

Content-based recommender system

In content-based recommender systems, all the data items are collected into different item profiles based on their description or features. For example, in the case of a book, the features will be author, publisher, etc. In the case of a movie, the features will be the movie director, actor, etc. When a user gives a positive rating to an item, then the other items present in that item profile are aggregated together to build a user profile. This user profile combines all the item profiles, whose items are rated positively by the user. Items present in this user profile are then recommended to the user, as shown in Fig.  2 .

figure 2

One drawback of this approach is that it demands in-depth knowledge of the item features for an accurate recommendation. This knowledge or information may not be always available for all items. Also, this approach has limited capacity to expand on the users' existing choices or interests. However, this approach has many advantages. As user preferences tend to change with time, this approach has the quick capability of dynamically adapting itself to the changing user preferences. Since one user profile is specific only to that user, this algorithm does not require the profile details of any other users because they provide no influence in the recommendation process. This ensures the security and privacy of user data. If new items have sufficient description, content-based techniques can overcome the cold-start problem i.e., this technique can recommend an item even when that item has not been previously rated by any user. Content-based filtering approaches are more common in systems like personalized news recommender systems, publications, web pages recommender systems, etc.

Collaborative filtering-based recommender system

Collaborative approaches make use of the measure of similarity between users. This technique starts with finding a group or collection of user X whose preferences, likes, and dislikes are similar to that of user A. X is called the neighbourhood of A. The new items which are liked by most of the users in X are then recommended to user A. The efficiency of a collaborative algorithm depends on how accurately the algorithm can find the neighbourhood of the target user. Traditionally collaborative filtering-based systems suffer from the cold-start problem and privacy concerns as there is a need to share user data. However, collaborative filtering approaches do not require any knowledge of item features for generating a recommendation. Also, this approach can help to expand on the user’s existing interests by discovering new items. Collaborative approaches are again divided into two types: memory-based approaches and model-based approaches.

Memory-based collaborative approaches recommend new items by taking into consideration the preferences of its neighbourhood. They make use of the utility matrix directly for prediction. In this approach, the first step is to build a model. The model is equal to a function that takes the utility matrix as input.

Model = f (utility matrix)

Then recommendations are made based on a function that takes the model and user profile as input. Here we can make recommendations only to users whose user profile belongs to the utility matrix. Therefore, to make recommendations for a new user, the user profile must be added to the utility matrix, and the similarity matrix should be recomputed, which makes this technique computation heavy.

Recommendation = f (defined model, user profile) where user profile  ∈  utility matrix

Memory-based collaborative approaches are again sub-divided into two types: user-based collaborative filtering and item-based collaborative filtering. In the user-based approach, the user rating of a new item is calculated by finding other users from the user neighbourhood who has previously rated that same item. If a new item receives positive ratings from the user neighbourhood, the new item is recommended to the user. Figure  3 depicts the user-based filtering approach.

figure 3

User-based collaborative filtering

In the item-based approach, an item-neighbourhood is built consisting of all similar items which the user has rated previously. Then that user’s rating for a different new item is predicted by calculating the weighted average of all ratings present in a similar item-neighbourhood as shown in Fig.  4 .

figure 4

Item-based collaborative filtering

Model-based systems use various data mining and machine learning algorithms to develop a model for predicting the user’s rating for an unrated item. They do not rely on the complete dataset when recommendations are computed but extract features from the dataset to compute a model. Hence the name, model-based technique. These techniques also need two steps for prediction—the first step is to build the model, and the second step is to predict ratings using a function (f) which takes the model defined in the first step and the user profile as input.

Recommendation = f (defined model, user profile) where user profile  ∉  utility matrix

Model-based techniques do not require adding the user profile of a new user into the utility matrix before making predictions. We can make recommendations even to users that are not present in the model. Model-based systems are more efficient for group recommendations. They can quickly recommend a group of items by using the pre-trained model. The accuracy of this technique largely relies on the efficiency of the underlying learning algorithm used to create the model. Model-based techniques are capable of solving some traditional problems of recommender systems such as sparsity and scalability by employing dimensionality reduction techniques [ 86 ] and model learning techniques.

Hybrid filtering

A hybrid technique is an aggregation of two or more techniques employed together for addressing the limitations of individual recommender techniques. The incorporation of different techniques can be performed in various ways. A hybrid algorithm may incorporate the results achieved from separate techniques, or it can use content-based filtering in a collaborative method or use a collaborative filtering technique in a content-based method. This hybrid incorporation of different techniques generally results in increased performance and increased accuracy in many recommender applications. Some of the hybridization approaches are meta-level, feature-augmentation, feature-combination, mixed hybridization, cascade hybridization, switching hybridization and weighted hybridization [ 86 ]. Table 2 describes these approaches.

Recommender system challenges

This section briefly describes the various challenges present in current recommender systems and offers different solutions to overcome these challenges.

Cold start problem

The cold start problem appears when the recommender system cannot draw any inference from the existing data, which is insufficient. Cold start refers to a condition when the system cannot produce efficient recommendations for the cold (or new) users who have not rated any item or have rated a very few items. It generally arises when a new user enters the system or new items (or products) are inserted into the database. Some solutions to this problem are as follows: (a) Ask new users to explicitly mention their item preference. (b) Ask a new user to rate some items at the beginning. (c) Collect demographic information (or meta-data) from the user and recommend items accordingly.

Shilling attack problem

This problem arises when a malicious user fakes his identity and enters the system to give false item ratings [ 87 ]. Such a situation occurs when the malicious user wants to either increase or decrease some item’s popularity by causing a bias on selected target items. Shilling attacks greatly reduce the reliability of the system. One solution to this problem is to detect the attackers quickly and remove the fake ratings and fake user profiles from the system.

Synonymy problem

This problem arises when similar or related items have different entries or names, or when the same item is represented by two or more names in the system [ 78 ]. For example, babywear and baby cloth. Many recommender systems fail to distinguish these differences, hence reducing their recommendation accuracy. To alleviate this problem many methods are used such as demographic filtering, automatic term expansion and Singular Value Decomposition [ 76 ].

Latency problem

The latency problem is specific to collaborative filtering approaches and occurs when new items are frequently inserted into the database. This problem is characterized by the system’s failure to recommend new items. This happens because new items must be reviewed before they can be recommended in a collaborative filtering environment. Using content-based filtering may resolve this issue, but it may introduce overspecialization and decrease the computing time and system performance. To increase performance, the calculations can be done in an offline environment and clustering-based techniques can be used [ 76 ].

Sparsity problem

Data sparsity is a common problem in large scale data analysis, which arises when certain expected values are missing in the dataset. In the case of recommender systems, this situation occurs when the active users rate very few items. This reduces the recommendation accuracy. To alleviate this problem several techniques can be used such as demographic filtering, singular value decomposition and using model-based collaborative techniques.

Grey sheep problem

The grey sheep problem is specific to pure collaborative filtering approaches where the feedback given by one user do not match any user neighbourhood. In this situation, the system fails to accurately predict relevant items for that user. This problem can be resolved by using pure content-based approaches where predictions are made based on the user’s profile and item properties.

Scalability problem

Recommender systems, especially those employing collaborative filtering techniques, require large amounts of training data, which cause scalability problems. The scalability problem arises when the amount of data used as input to a recommender system increases quickly. In this era of big data, more and more items and users are rapidly getting added to the system and this problem is becoming common in recommender systems. Two common approaches used to solve the scalability problem is dimensionality reduction and using clustering-based techniques to find users in tiny clusters instead of the complete database.

Methodology

The purpose of this study is to understand the research trends in the field of recommender systems. The nature of research in recommender systems is such that it is difficult to confine each paper to a specific discipline. This can be further understood by the fact that research papers on recommender systems are scattered across various journals such as computer science, management, marketing, information technology and information science. Hence, this literature review is conducted over a wide range of electronic journals and research databases such as ACM Portal, IEEE/IEE Library, Google Scholars and Science Direct [ 88 ].

The search process of online research articles was performed based on 6 descriptors: “Recommender systems”, “Recommendation systems”, “Movie Recommend*”, “Music Recommend*”, “Personalized Recommend*”, “Hybrid Recommend*”. The following research papers described below were excluded from our research:

News articles.

Master’s dissertations.

Non-English papers.

Unpublished papers.

Research papers published before 2011.

We have screened a total of 350 articles based on their abstracts and content. However, only research papers that described how recommender systems can be applied were chosen. Finally, 60 papers were selected from top international journals indexed in Scopus or E-SCI in 2021. We now present the PRISMA flowchart of the inclusion and exclusion process in Fig.  5 .

figure 5

PRISMA flowchart of the inclusion and exclusion process. Abstract and content not suitable to the study: * The use or application of the recommender system is not specified: **

Each paper was carefully reviewed and classified into 6 categories in the application fields and 3 categories in the techniques used to develop the system. The classification framework is presented in Fig.  6 .

figure 6

Classification framework

The number of relevant articles come from Expert Systems with Applications (23%), followed by IEEE (17%), Knowledge-Based System (17%) and Others (43%). Table 3 depicts the article distribution by journal title and Table 4 depicts the sector-wise article distribution.

Both forward and backward searching techniques were implemented to establish that the review of 60 chosen articles can represent the domain literature. Hence, this paper can demonstrate its validity and reliability as a literature review.

Review on state-of-the-art recommender systems

This section presents a state-of-art literature review followed by a chronological review of the various existing recommender systems.

Literature review

In 2011, Castellano et al. [ 1 ] developed a “NEuro-fuzzy WEb Recommendation (NEWER)” system for exploiting the possibility of combining computational intelligence and user preference for suggesting interesting web pages to the user in a dynamic environment. It considered a set of fuzzy rules to express the correlations between user relevance and categories of pages. Crespo et al. [ 2 ] presented a recommender system for distance education over internet. It aims to recommend e-books to students using data from user interaction. The system was developed using a collaborative approach and focused on solving the data overload problem in big digital content. Lin et al. [ 3 ] have put forward a recommender system for automatic vending machines using Genetic algorithm (GA), k-means, Decision Tree (DT) and Bayesian Network (BN). It aimed at recommending localized products by developing a hybrid model combining statistical methods, classification methods, clustering methods, and meta-heuristic methods. Wang and Wu [ 4 ] have implemented a ubiquitous learning system for providing personalized learning assistance to the learners by combining the recommendation algorithm with a context-aware technique. It employed the Association Rule Mining (ARM) technique and aimed to increase the effectiveness of the learner’s learning. García-Crespo et al. [ 5 ] presented a “semantic hotel” recommender system by considering the experiences of consumers using a fuzzy logic approach. The system considered both hotel and customer characteristics. Dong et al. [ 6 ] proposed a structure for a service-concept recommender system using a semantic similarity model by integrating the techniques from the view of an ontology structure-oriented metric and a concept content-oriented metric. The system was able to deliver optimal performance when compared with similar recommender systems. Li et al. [ 7 ] developed a Fuzzy linguistic modelling-based recommender system for assisting users to find experts in knowledge management systems. The developed system was applied to the aircraft industry where it demonstrated efficient and feasible performance. Lorenzi et al. [ 8 ] presented an “assumption-based multiagent” system to make travel package recommendations using user preferences in the tourism industry. It performed different tasks like discovering, filtering, and integrating specific information for building a travel package following the user requirement. Huang et al. [ 9 ] proposed a context-aware recommender system through the extraction, evaluation and incorporation of contextual information gathered using the collaborative filtering and rough set model.

In 2012, Chen et al. [ 10 ] presented a diabetes medication recommender model by using “Semantic Web Rule Language (SWRL) and Java Expert System Shell (JESS)” for aggregating suitable prescriptions for the patients. It aimed at selecting the most suitable drugs from the list of specific drugs. Mohanraj et al. [ 11 ] developed the “Ontology-driven bee’s foraging approach (ODBFA)” to accurately predict the online navigations most likely to be visited by a user. The self-adaptive system is intended to capture the various requirements of the online user by using a scoring technique and by performing a similarity comparison. Hsu et al. [ 12 ] proposed a “personalized auxiliary material” recommender system by considering the specific course topics, individual learning styles, complexity of the auxiliary materials using an artificial bee colony algorithm. Gemmell et al. [ 13 ] demonstrated a solution for the problem of resource recommendation in social annotation systems. The model was developed using a linear-weighted hybrid method which was capable of providing recommendations under different constraints. Choi et al. [ 14 ] proposed one “Hybrid Online-Product rEcommendation (HOPE) system” by the integration of collaborative filtering through sequential pattern analysis-based recommendations and implicit ratings. Garibaldi et al. [ 15 ] put forward a technique for incorporating the variability in a fuzzy inference model by using non-stationary fuzzy sets for replicating the variabilities of a human. This model was applied to a decision problem for treatment recommendations of post-operative breast cancer.

In 2013, Salehi and Kmalabadi [ 16 ] proposed an e-learning material recommender system by “modelling of materials in a multidimensional space of material’s attribute”. It employed both content and collaborative filtering. Aher and Lobo [ 17 ] introduced a course recommender system using data mining techniques such as simple K-means clustering and Association Rule Mining (ARM) algorithm. The proposed e-learning system was successfully demonstrated for “MOOC (Massively Open Online Courses)”. Kardan and Ebrahimi [ 18 ] developed a hybrid recommender system for recommending posts in asynchronous discussion groups. The system was built combining both collaborative filtering and content-based filtering. It considered implicit user data to compute the user similarity with various groups, for recommending suitable posts and contents to its users. Chang et al. [ 19 ] adopted a cloud computing technology for building a TV program recommender system. The system designed for digital TV programs was implemented using Hadoop Fair Scheduler (HFC), K-means clustering and k-nearest neighbour (KNN) algorithms. It was successful in processing huge amounts of real-time user data. Lucas et al. [ 20 ] implemented a recommender model for assisting a tourism application by using associative classification and fuzzy logic to predict the context. Niu et al. [ 21 ] introduced “Affivir: An Affect-based Internet Video Recommendation System” which was developed by calculating user preferences and by using spectral clustering. This model recommended videos with similar effects, which was processed to get optimal results with dynamic adjustments of recommendation constraints.

In 2014, Liu et al. [ 22 ] implemented a new route recommendation model for offering personalized and real-time route recommendations for self-driven tourists to minimize the queuing time and traffic jams infamous tourist places. Recommendations were carried out by considering the preferences of users. Bakshi et al. [ 23 ] proposed an unsupervised learning-based recommender model for solving the scalability problem of recommender systems. The algorithm used transitive similarities along with Particle Swarm Optimization (PSO) technique for discovering the global neighbours. Kim and Shim [ 24 ] proposed a recommender system based on “latent Dirichlet allocation using probabilistic modelling for Twitter” that could recommend the top-K tweets for a user to read, and the top-K users to follow. The model parameters were learned from an inference technique by using the differential Expectation–Maximization (EM) algorithm. Wang et al. [ 25 ] developed a hybrid-movie recommender model by aggregating a genetic algorithm (GA) with improved K-means and Principal Component Analysis (PCA) technique. It was able to offer intelligent movie recommendations with personalized suggestions. Kolomvatsos et al. [ 26 ] proposed a recommender system by considering an optimal stopping theory for delivering books or music recommendations to the users. Gottschlich et al. [ 27 ] proposed a decision support system for stock investment recommendations. It computed the output by considering the overall crowd’s recommendations. Torshizi et al. [ 28 ] have introduced a hybrid recommender system to determine the severity level of a medical condition. It could recommend suitable therapies for patients suffering from Benign Prostatic Hyperplasia.

In 2015, Zahálka et al. [ 29 ] proposed a venue recommender: “City Melange”. It was an interactive content-based model which used the convolutional deep-net features of the visual domain and the linear Support Vector Machine (SVM) model to capture the semantic information and extract latent topics. Sankar et al. [ 30 ] have proposed a stock recommender system based on the stock holding portfolio of trusted mutual funds. The system employed the collaborative filtering approach along with social network analysis for offering a decision support system to build a trust-based recommendation model. Chen et al. [ 31 ] have put forward a novel movie recommender system by applying the “artificial immune network to collaborative filtering” technique. It computed the affinity of an antigen and the affinity between an antibody and antigen. Based on this computation a similarity estimation formula was introduced which was used for the movie recommendation process. Wu et al. [ 32 ] have examined the technique of data fusion for increasing the efficiency of item recommender systems. It employed a hybrid linear combination model and used a collaborative tagging system. Yeh and Cheng [ 33 ] have proposed a recommender system for tourist attractions by constructing the “elicitation mechanism using the Delphi panel method and matrix construction mechanism using the repertory grids”, which was developed by considering the user preference and expert knowledge.

In 2016, Liao et al. [ 34 ] proposed a recommender model for online customers using a rough set association rule. The model computed the probable behavioural variations of online consumers and provided product category recommendations for e-commerce platforms. Li et al. [ 35 ] have suggested a movie recommender system based on user feedback collected from microblogs and social networks. It employed the sentiment-aware association rule mining algorithm for recommendations using the prior information of frequent program patterns, program metadata similarity and program view logs. Wu et al. [ 36 ] have developed a recommender system for social media platforms by aggregating the technique of Social Matrix Factorization (SMF) and Collaborative Topic Regression (CTR). The model was able to compute the ratings of users to items for making recommendations. For improving the recommendation quality, it gathered information from multiple sources such as item properties, social networks, feedback, etc. Adeniyi et al. [ 37 ] put forward a study of automated web-usage data mining and developed a recommender system that was tested in both real-time and online for identifying the visitor’s or client’s clickstream data.

In 2017, Rawat and Kankanhalli [ 38 ] have proposed a viewpoint recommender system called “ClickSmart” for assisting mobile users to capture high-quality photographs at famous tourist places. Yang et al. [ 39 ] proposed a gradient boosting-based job recommendation system for satisfying the cost-sensitive requirements of the users. The hybrid algorithm aimed to reduce the rate of unnecessary job recommendations. Lee et al. [ 40 ] proposed a music streaming recommender system based on smartphone activity usage. The proposed system benefitted by using feature selection approaches with machine learning techniques such as Naive Bayes (NB), Support Vector Machine (SVM), Multi-layer Perception (MLP), Instance-based k -Nearest Neighbour (IBK), and Random Forest (RF) for performing the activity detection from the mobile signals. Wei et al. [ 41 ] have proposed a new stacked denoising autoencoder (SDAE) based recommender system for cold items. The algorithm employed deep learning and collaborative filtering method to predict the unknown ratings.

In 2018, Li et al. [ 42 ] have developed a recommendation algorithm using Weighted Linear Regression Models (WLRRS). The proposed system was put to experiment using the MovieLens dataset and it presented better classification and predictive accuracy. Mezei and Nikou [ 43 ] presented a mobile health and wellness recommender system based on fuzzy optimization. It could recommend a collection of actions to be taken by the user to improve the user’s health condition. Recommendations were made considering the user’s physical activities and preferences. Ayata et al. [ 44 ] proposed a music recommendation model based on the user emotions captured through wearable physiological sensors. The emotion detection algorithm employed different machine learning algorithms like SVM, RF, KNN and decision tree (DT) algorithms to predict the emotions from the changing electrical signals gathered from the wearable sensors. Zhao et al. [ 45 ] developed a multimodal learning-based, social-aware movie recommender system. The model was able to successfully resolve the sparsity problem of recommender systems. The algorithm developed a heterogeneous network by exploiting the movie-poster image and textual description of each movie based on the social relationships and user ratings.

In 2019, Hammou et al. [ 46 ] proposed a Big Data recommendation algorithm capable of handling large scale data. The system employed random forest and matrix factorization through a data partitioning scheme. It was then used for generating recommendations based on user rating and preference for each item. The proposed system outperformed existing systems in terms of accuracy and speed. Zhao et al. [ 47 ] have put forward a hybrid initialization method for social network recommender systems. The algorithm employed denoising autoencoder (DAE) neural network-based initialization method (ANNInit) and attribute mapping. Bhaskaran and Santhi [ 48 ] have developed a hybrid, trust-based e-learning recommender system using cloud computing. The proposed algorithm was capable of learning online user activities by using the Firefly Algorithm (FA) and K-means clustering. Afolabi and Toivanen [ 59 ] have suggested an integrated recommender model based on collaborative filtering. The proposed model “Connected Health for Effective Management of Chronic Diseases”, aimed for integrating recommender systems for better decision-making in the process of disease management. He et al. [ 60 ] proposed a movie recommender system called “HI2Rec” which explored the usage of collaborative filtering and heterogeneous information for making movie recommendations. The model used the knowledge representation learning approach to embed movie-related information gathered from different sources.

In 2020, Han et al. [ 49 ] have proposed one Internet of Things (IoT)-based cancer rehabilitation recommendation system using the Beetle Antennae Search (BAS) algorithm. It presented the patients with a solution for the problem of optimal nutrition program by considering the objective function as the recurrence time. Kang et al. [ 50 ] have presented a recommender system for personalized advertisements in Online Broadcasting based on a tree model. Recommendations were generated in real-time by considering the user preferences to minimize the overhead of preference prediction and using a HashMap along with the tree characteristics. Ullah et al. [ 51 ] have implemented an image-based service recommendation model for online shopping based random forest and Convolutional Neural Networks (CNN). The model used JPEG coefficients to achieve an accurate prediction rate. Cai et al. [ 52 ] proposed a new hybrid recommender model using a many-objective evolutionary algorithm (MaOEA). The proposed algorithm was successful in optimizing the novelty, diversity, and accuracy of recommendations. Esteban et al. [ 53 ] have implemented a hybrid multi-criteria recommendation system concerned with students’ academic performance, personal interests, and course selection. The system was developed using a Genetic Algorithm (GA) and aimed at helping university students. It combined both course information and student information for increasing system performance and the reliability of the recommendations. Mondal et al. [ 54 ] have built a multilayer, graph data model-based doctor recommendation system by exploiting the trust concept between a patient-doctor relationship. The proposed system showed good results in practical applications.

In 2021, Dhelim et al. [ 55 ] have developed a personality-based product recommending model using the techniques of meta path discovery and user interest mining. This model showed better results when compared to session-based and deep learning models. Bhalse et al. [ 56 ] proposed a web-based movie recommendation system based on collaborative filtering using Singular Value Decomposition (SVD), collaborative filtering and cosine similarity (CS) for addressing the sparsity problem of recommender systems. It suggested a recommendation list by considering the content information of movies. Similarly, to solve both sparsity and cold-start problems Ke et al. [ 57 ] proposed a dynamic goods recommendation system based on reinforcement learning. The proposed system was capable of learning from the reduced entropy loss error on real-time applications. Chen et al. [ 58 ] have presented a movie recommender model combining various techniques like user interest with category-level representation, neighbour-assisted representation, user interest with latent representation and item-level representation using Feed-forward Neural Network (FNN).

Comparative chronological review

A comparative chronological review to compare the total contributions on various recommender systems in the past 10 years is given in Fig.  7 .

figure 7

Comparative chronological review of recommender systems under diverse applications

This review puts forward a comparison of the number of research works proposed in the domain of recommender systems from the year 2011 to 2021 using various deep learning and machine learning-based approaches. Research articles are categorized based on the recommender system classification framework as shown in Table 5 . The articles are ordered according to their year of publication. There are two key concepts: Application fields and techniques used. The application fields of recommender systems are divided into six different fields, viz. entertainment, health, tourism, web/e-commerce, education and social media/others.

Algorithmic categorization, simulation platforms and applications considered for various recommender systems

This section analyses different methods like deep learning, machine learning, clustering and meta-heuristic-based-approaches used in the development of recommender systems. The algorithmic categorization of different recommender systems is given in Fig.  8 .

figure 8

Algorithmic categorization of different recommender systems

Categorization is done based on content-based, collaborative filtering-based, and optimization-based approaches. In [ 8 ], a content-based filtering technique was employed for increasing the ability to trust other agents and for improving the exchange of information by trust degree. In [ 16 ], it was applied to enhance the quality of recommendations using the account attributes of the material. It achieved better performance concerning with F1-score, recall and precision. In [ 18 ], this technique was able to capture the implicit user feedback, increasing the overall accuracy of the proposed model. The content-based filtering in [ 30 ] was able to increase the accuracy and performance of a stock recommender system by using the “trust factor” for making decisions.

Different collaborative filtering approaches are utilized in recent studies, which are categorized as follows:

Model-based techniques

Neuro-Fuzzy [ 1 ] based technique helps in discovering the association between user categories and item relevance. It is also simple to understand. K-Means Clustering [ 2 , 19 , 25 , 48 ] is efficient for large scale datasets. It is simple to implement and gives a fast convergence rate. It also offers automatic recovery from failures. The decision tree [ 2 , 44 ] technique is easy to interpret. It can be used for solving the classic regression and classification problems in recommender systems. Bayesian Network [ 3 ] is a probabilistic technique used to solve classification challenges. It is based on the theory of Bayes theorem and conditional probability. Association Rule Mining (ARM) techniques [ 4 , 17 , 35 ] extract rules for projecting the occurrence of an item by considering the existence of other items in a transaction. This method uses the association rules to create a more suitable representation of data and helps in increasing the model performance and storage efficiency. Fuzzy Logic [ 5 , 7 , 15 , 20 , 28 , 43 ] techniques use a set of flexible rules. It focuses on solving complex real-time problems having an inaccurate spectrum of data. This technique provides scalability and helps in increasing the overall model performance for recommender systems. The semantic similarity [ 6 ] technique is used for describing a topological similarity to define the distance among the concepts and terms through ontologies. It measures the similarity information for increasing the efficiency of recommender systems. Rough set [ 9 , 34 ] techniques use probability distributions for solving the challenges of existing recommender models. Semantic web rule language [ 10 ] can efficiently extract the dataset features and increase the model efficiency. Linear programming-based approaches [ 13 , 42 ] are employed for achieving quality decision making in recommender models. Sequential pattern analysis [ 14 ] is applied to find suitable patterns among data items. This helps in increasing model efficiency. The probabilistic model [ 24 ] is a famous tool to handle uncertainty in risk computations and performance assessment. It offers better decision-making capabilities. K-nearest neighbours (KNN) [ 19 , 37 , 44 ] technique provides faster computation time, simplicity and ease of interpretation. They are good for classification and regression-based problems and offers more accuracy. Spectral clustering [ 21 ] is also called graph clustering or similarity-based clustering, which mainly focuses on reducing the space dimensionality in identifying the dataset items. Stochastic learning algorithm [ 26 ] solves the real-time challenges of recommender systems. Linear SVM [ 29 , 44 ] efficiently solves the high dimensional problems related to recommender systems. It is a memory-efficient method and works well with a large number of samples having relative separation among the classes. This method has been shown to perform well even when new or unfamiliar data is added. Relational Functional Gradient Boosting [ 39 ] technique efficiently works on the relational dependency of data, which is useful for statical relational learning for collaborative-based recommender systems. Ensemble learning [ 40 ] combines the forecast of two or more models and aims to achieve better performance than any of the single contributing models. It also helps in reducing overfitting problems, which are common in recommender systems.

SDAE [ 41 ] is used for learning the non-linear transformations with different filters for finding suitable data. This aids in increasing the performance of recommender models. Multimodal network learning [ 45 ] is efficient for multi-modal data, representing a combined representation of diverse modalities. Random forest [ 46 , 51 ] is a commonly used approach in comparison with other classifiers. It has been shown to increase accuracy when handling big data. This technique is a collection of decision trees to minimize variance through training on diverse data samples. ANNInit [ 47 ] is a type of artificial neural network-based technique that has the capability of self-learning and generating efficient results. It is independent of the data type and can learn data patterns automatically. HashMap [ 50 ] gives faster access to elements owing to the hashing methodology, which decreases the data processing time and increases the performance of the system. CNN [ 51 ] technique can automatically fetch the significant features of a dataset without any supervision. It is a computationally efficient method and provides accurate recommendations. This technique is also simple and fast for implementation. Multilayer graph data model [ 54 ] is efficient for real-time applications and minimizes the access time through mapping the correlation as edges among nodes and provides superior performance. Singular Value Decomposition [ 56 ] can simplify the input data and increase the efficiency of recommendations by eliminating the noise present in data. Reinforcement learning [ 57 ] is efficient for practical scenarios of recommender systems having large data sizes. It is capable of boosting the model performance by increasing the model accuracy even for large scale datasets. FNN [ 58 ] is one of the artificial neural network techniques which can learn non-linear and complex relationships between items. It has demonstrated a good performance increase when employed in different recommender systems. Knowledge representation learning [ 60 ] systems aim to simplify the model development process by increasing the acquisition efficiency, inferential efficiency, inferential adequacy and representation adequacy. User-based approaches [ 2 , 55 , 59 ] specialize in detecting user-related meta-data which is employed to increase the overall model performance. This technique is more suitable for real-time applications where it can capture user feedback and use it to increase the user experience.

Optimization-based techniques

The Foraging Bees [ 11 ] technique enables both functional and combinational optimization for random searching in recommender models. Artificial bee colony [ 12 ] is a swarm-based meta-heuristic technique that provides features like faster convergence rate, the ability to handle the objective with stochastic nature, ease for incorporating with other algorithms, usage of fewer control parameters, strong robustness, high flexibility and simplicity. Particle Swarm Optimization [ 23 ] is a computation optimization technique that offers better computational efficiency, robustness in control parameters, and is easy and simple to implement in recommender systems. Portfolio optimization algorithm [ 27 ] is a subclass of optimization algorithms that find its application in stock investment recommender systems. It works well in real-time and helps in the diversification of the portfolio for maximum profit. The artificial immune system [ 31 ]a is computationally intelligent machine learning technique. This technique can learn new patterns in the data and optimize the overall system parameters. Expectation maximization (EM) [ 32 , 36 , 38 ] is an iterative algorithm that guarantees the likelihood of finding the maximum parameters when the input variables are unknown. Delphi panel and repertory grid [ 33 ] offers efficient decision making by solving the dimensionality problem and data sparsity issues of recommender systems. The Firefly algorithm (FA) [ 48 ] provides fast results and increases recommendation efficiency. It is capable of reducing the number of iterations required to solve specific recommender problems. It also provides both local and global sets of solutions. Beetle Antennae Search (BAS) [ 49 ] offers superior search accuracy and maintains less time complexity that promotes the performance of recommendations. Many-objective evolutionary algorithm (MaOEA) [ 52 ] is applicable for real-time, multi-objective, search-related recommender systems. The introduction of a local search operator increases the convergence rate and gets suitable results. Genetic Algorithm (GA) [ 2 , 22 , 25 , 53 ] based techniques are used to solve the multi-objective optimization problems of recommender systems. They employ probabilistic transition rules and have a simpler operation that provides better recommender performance.

Features and challenges

The features and challenges of the existing recommender models are given in Table 6 .

Simulation platforms

The various simulation platforms used for developing different recommender systems with different applications are given in Fig.  9 .

figure 9

Simulation platforms used for developing different recommender systems

Here, the Java platform is used in 20% of the contributions, MATLAB is implemented in 7% of the contributions, different fold cross-validation are used in 8% of the contributions, 7% of the contributions are utilized by the python platform, 3% of the contributions employ R-programming and 1% of the contributions are developed by Tensorflow, Weka and Android environments respectively. Other simulation platforms like Facebook, web UI (User Interface), real-time environments, etc. are used in 50% of the contributions. Table 7 describes some simulation platforms commonly used for developing recommender systems.

Application focused and dataset description

This section provides an analysis of the different applications focused on a set of recent recommender systems and their dataset details.

Recent recommender systems were analysed and found that 11% of the contributions are focused on the domain of healthcare, 10% of the contributions are on movie recommender systems, 5% of the contributions come from music recommender systems, 6% of the contributions are focused on e-learning recommender systems, 8% of the contributions are used for online product recommender systems, 3% of the contributions are focused on book recommendations and 1% of the contributions are focused on Job and knowledge management recommender systems. 5% of the contributions concentrated on social network recommender systems, 10% of the contributions are focused on tourist and hotels recommender systems, 6% of the contributions are employed for stock recommender systems, and 3% of the contributions contributed for video recommender systems. The remaining 12% of contributions are miscellaneous recommender systems like Twitter, venue-based recommender systems, etc. Similarly, different datasets are gathered for recommender systems based on their application types. A detailed description is provided in Table 8 .

Performance analysis of state-of-art recommender systems

The performance evaluation metrics used for the analysis of different recommender systems is depicted in Table 9 . From the set of research works, 35% of the works use recall measure, 16% of the works employ Mean Absolute Error (MAE), 11% of the works take Root Mean Square Error (RMSE), 41% of the papers consider precision, 30% of the contributions analyse F1-measure, 31% of the works apply accuracy and 6% of the works employ coverage measure to validate the performance of the recommender systems. Moreover, some additional measures are also considered for validating the performance in a few applications.

Research gaps and challenges

In the recent decade, recommender systems have performed well in solving the problem of information overload and has become the more appropriate tool for multiple areas such as psychology, mathematics, computer science, etc. [ 80 ]. However, current recommender systems face a variety of challenges which are stated as follows, and discussed below:

Deployment challenges such as cold start, scalability, sparsity, etc. are already discussed in Sect. 3.

Challenges faced when employing different recommender algorithms for different applications.

Challenges in collecting implicit user data

Challenges in handling real-time user feedback.

Challenges faced in choosing the correct implementation techniques.

Challenges faced in measuring system performance.

Challenges in implementing recommender system for diverse applications.

Numerous recommender algorithms have been proposed on novel emerging dimensions which focus on addressing the existing limitations of recommender systems. A good recommender system must increase the recommendation quality based on user preferences. However, a specific recommender algorithm is not always guaranteed to perform equally for different applications. This encourages the possibility of employing different recommender algorithms for different applications, which brings along a lot of challenges. There is a need for more research to alleviate these challenges. Also, there is a large scope of research in recommender applications that incorporate information from different interactive online sites like Facebook, Twitter, shopping sites, etc. Some other areas for emerging research may be in the fields of knowledge-based recommender systems, methods for seamlessly processing implicit user data and handling real-time user feedback to recommend items in a dynamic environment.

Some of the other research areas like deep learning-based recommender systems, demographic filtering, group recommenders, cross-domain techniques for recommender systems, and dimensionality reduction techniques are also further required to be studied [ 83 ]. Deep learning-based recommender systems have recently gained much popularity. Future research areas in this field can integrate the well-performing deep learning models with new variants of hybrid meta-heuristic approaches.

During this review, it was observed that even though recent recommender systems have demonstrated good performance, there is no single standardized criteria or method which could be used to evaluate the performance of all recommender systems. System performance is generally measured by different evaluation matrices which makes it difficult to compare. The application of recommender systems in real-time applications is growing. User satisfaction and personalization play a very important role in the success of such recommender systems. There is a need for some new evaluation criteria which can evaluate the level of user satisfaction in real-time. New research should focus on capturing real-time user feedback and use the information to change the recommendation process accordingly. This will aid in increasing the quality of recommendations.

Conclusion and future scope

Recommender systems have attracted the attention of researchers and academicians. In this paper, we have identified and prudently reviewed research papers on recommender systems focusing on diverse applications, which were published between 2011 and 2021. This review has gathered diverse details like different application fields, techniques used, simulation tools used, diverse applications focused, performance metrics, datasets used, system features, and challenges of different recommender systems. Further, the research gaps and challenges were put forward to explore the future research perspective on recommender systems. Overall, this paper provides a comprehensive understanding of the trend of recommender systems-related research and to provides researchers with insight and future direction on recommender systems. The results of this study have several practical and significant implications:

Based on the recent-past publication rates, we feel that the research of recommender systems will significantly grow in the future.

A large number of research papers were identified in movie recommendations, whereas health, tourism and education-related recommender systems were identified in very few numbers. This is due to the availability of movie datasets in the public domain. Therefore, it is necessary to develop datasets in other fields also.

There is no standard measure to compute the performance of recommender systems. Among 60 papers, 21 used recall, 10 used MAE, 25 used precision, 18 used F1-measure, 19 used accuracy and only 7 used RMSE to calculate system performance. Very few systems were found to excel in two or more matrices.

Java and Python (with a combined contribution of 27%) are the most common programming languages used to develop recommender systems. This is due to the availability of a large number of standard java and python libraries which aid in the development process.

Recently a large number of hybrid and optimizations techniques are being proposed for recommender systems. The performance of a recommender system can be greatly improved by applying optimization techniques.

There is a large scope of research in using neural networks and deep learning-based methods for developing recommender systems. Systems developed using these methods are found to achieve high-performance accuracy.

This research will provide a guideline for future research in the domain of recommender systems. However, this research has some limitations. Firstly, due to the limited amount of manpower and time, we have only reviewed papers published in journals focusing on computer science, management and medicine. Secondly, we have reviewed only English papers. New research may extend this study to cover other journals and non-English papers. Finally, this review was conducted based on a search on only six descriptors: “Recommender systems”, “Recommendation systems”, “Movie Recommend*”, “Music Recommend*”, “Personalized Recommend*” and “Hybrid Recommend*”. Research papers that did not include these keywords were not considered. Future research can include adding some additional descriptors and keywords for searching. This will allow extending the research to cover more diverse articles on recommender systems.

Availability of data and materials

Not applicable.

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Blockchain booms, risks loom: the ai rescue mission in smart contract auditing, developing incident response plans for insider threats, weis wave: revolutionizing market analysis, top machine learning (ml) research papers released in 2022.

For every Machine Learning (ML) enthusiast, we bring you a curated list of the major breakthroughs in ML research in 2022.

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Machine learning (ML) is gaining much traction in recent years owing to the disruption and development it brings in enhancing existing technologies. Every month, hundreds of ML papers from various organizations and universities get uploaded on the internet to share the latest breakthroughs in this domain. As the year ends, we bring you the Top 22 ML research papers of 2022 that created a huge impact in the industry. The following list does not reflect the ranking of the papers, and they have been selected on the basis of the recognitions and awards received at international conferences in machine learning.

  • Bootstrapped Meta-Learning

Meta-learning is a promising field that investigates ways to enable machine learners or RL agents (which include hyperparameters) to learn how to learn in a quicker and more robust manner, and it is a crucial study area for enhancing the efficiency of AI agents.

This 2022 ML paper presents an algorithm that teaches the meta-learner how to overcome the meta-optimization challenge and myopic meta goals. The algorithm’s primary objective is meta-learning using gradients, which ensures improved performance. The research paper also examines the potential benefits due to bootstrapping. The authors highlight several interesting theoretical aspects of this algorithm, and the empirical results achieve new state-of-the-art (SOTA) on the ATARI ALE benchmark as well as increased efficiency in multitask learning.

  • Competition-level code generation with AlphaCode

One of the exciting uses for deep learning and large language models is programming. The rising need for coders has sparked the race to build tools that can increase developer productivity and provide non-developers with tools to create software. However, these models still perform badly when put to the test on more challenging, unforeseen issues that need more than just converting instructions into code.

The popular ML paper of 2022 introduces AlphaCode, a code generation system that, in simulated assessments of programming contests on the Codeforces platform, averaged a rating in the top 54.3%. The paper describes the architecture, training, and testing of the deep-learning model.

  • Restoring and attributing ancient texts using deep neural networks

The epigraphic evidence of the ancient Greek era — inscriptions created on durable materials such as stone and pottery —  had already been broken when it was discovered, rendering the inscribed writings incomprehensible. Machine learning can help in restoring, and identifying chronological and geographical origins of damaged inscriptions to help us better understand our past. 

This ML paper proposed a machine learning model built by DeepMind, Ithaca, for the textual restoration and geographical and chronological attribution of ancient Greek inscriptions. Ithaca was trained on a database of just under 80,000 inscriptions from the Packard Humanities Institute. It had a 62% accuracy rate compared to historians, who had a 25% accuracy rate on average. But when historians used Ithaca, they quickly achieved a 72% accuracy.

  • Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

Large neural networks use more resources to train hyperparameters since each time, the network must estimate which hyperparameters to utilize. This groundbreaking ML paper of 2022 suggests a novel zero-shot hyperparameter tuning paradigm for more effectively tuning massive neural networks. The research, co-authored by Microsoft Research and OpenAI, describes a novel method called µTransfer that leverages µP to zero-shot transfer hyperparameters from small models and produces nearly perfect HPs on large models without explicitly tuning them.

This method has been found to reduce the amount of trial and error necessary in the costly process of training large neural networks. By drastically lowering the need to predict which training hyperparameters to use, this approach speeds up research on massive neural networks like GPT-3 and perhaps its successors in the future.

  • PaLM: Scaling Language Modeling with Pathways 

Large neural networks trained for language synthesis and recognition have demonstrated outstanding results in various tasks in recent years. This trending 2022 ML paper introduced Pathways Language Model (PaLM), a 780 billion high-quality text token, and 540 billion parameter-dense decoder-only autoregressive transformer.

Although PaLM just uses a decoder and makes changes like SwiGLU Activation, Parallel Layers, Multi-Query Attention, RoPE Embeddings, Shared Input-Output Embeddings, and No Biases and Vocabulary, it is based on a typical transformer model architecture. The paper describes the company’s latest flagship surpassing several human baselines while achieving state-of-the-art in numerous zero, one, and few-shot NLP tasks.

  • Robust Speech Recognition via Large-Scale Weak Supervision

Machine learning developers have found it challenging to build speech-processing algorithms that are trained to predict a vast volume of audio transcripts on the internet. This year, OpenAI released Whisper , a new state-of-the-art (SotA) model in speech-to-text that can transcribe any audio to text and translate it into several languages. It has received 680,000 hours of training on a vast amount of voice data gathered from the internet. According to OpenAI, this model is robust to accents, background noise, and technical terminology. Additionally, it allows transcription into English from 99 different languages and translation into English from those languages.

The OpenAI ML paper mentions the author ensured that about one-third of the audio data is non-English. This helped the team outperform other supervised state-of-the-art models by maintaining a diversified dataset.

  • OPT: Open Pre-trained Transformer Language Models

Large language models have demonstrated extraordinary performance f on numerous tasks (e.g., zero and few-shot learning). However, these models are difficult to duplicate without considerable funding due to their high computing costs. Even while the public can occasionally interact with these models through paid APIs, complete research access is still only available from a select group of well-funded labs. This limited access has hindered researchers’ ability to comprehend how and why these language models work, which has stalled progress on initiatives to improve their robustness and reduce ethical drawbacks like bias and toxicity.

The popular 2022 ML paper introduces Open Pre-trained Transformers (OPT), a suite of decoder-only pre-trained transformers with 125 million to 175 billion parameters that the authors want to share freely and responsibly with interested academics. The biggest OPT model, OPT-175B (it is not included in the code repository but is accessible upon request), which is impressively proven to perform similarly to GPT-3 (which also has 175 billion parameters)  uses just 15% of GPT-3’s carbon footprint during development and training.

  • A Path Towards Autonomous Machine Intelligence

Yann LeCun is a prominent and respectable researcher in the field of artificial intelligence and machine learning. In June, his much-anticipated paper “ A Path Towards Autonomous Machine Intelligence ” was published on OpenReview. LeCun offered a number of approaches and architectures in his paper that might be combined and used to create self-supervised autonomous machines. 

He presented a modular architecture for autonomous machine intelligence that combines various models to operate as distinct elements of a machine’s brain and mirror the animal brain. Due to the differentiability of all the models, they are all interconnected to power certain brain-like activities, such as identification and environmental response. It incorporates ideas like a configurable predictive world model, behavior-driven through intrinsic motivation, and hierarchical joint embedding architectures trained with self-supervised learning. 

  • LaMDA: Language Models for Dialog Applications 

Despite tremendous advances in text generation, many of the chatbots available are still rather irritating and unhelpful. This 2022 ML paper from Google describes the LaMDA — short for “Language Model for Dialogue Applications” — system, which caused the uproar this summer when a former Google engineer, Blake Lemoine, alleged that it is sentient. LaMDA is a family of large language models for dialog applications built on Google’s Transformer architecture, which is known for its efficiency and speed in language tasks such as translation. The model’s ability to be adjusted using data that has been human-annotated and the capability of consulting external sources are its most intriguing features.

The model, which has 137 billion parameters, was pre-trained using 1.56 trillon words from publicly accessible conversation data and online publications. The model is also adjusted based on the three parameters of quality, safety, and groundedness.

  • Privacy for Free: How does Dataset Condensation Help Privacy?

One of the primary proposals in the award-winning ML paper is to use dataset condensation methods to retain data efficiency during model training while also providing membership privacy. The authors argue that dataset condensation, which was initially created to increase training effectiveness, is a better alternative to data generators for producing private data since it offers privacy for free. 

Though existing data generators are used to produce differentially private data for model training to minimize unintended data leakage, they result in high training costs or subpar generalization performance for the sake of data privacy. This study was published by Sony AI and received the Outstanding Paper Award at ICML 2022. 

  • TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data

The use of a model that converts time series into anomaly scores at each time step is essential in any system for detecting time series anomalies. Recognizing and diagnosing anomalies in multivariate time series data is critical for modern industrial applications. Unfortunately, developing a system capable of promptly and reliably identifying abnormal observations is challenging. This is attributed to a shortage of anomaly labels, excessive data volatility, and the expectations of modern applications for ultra-low inference times. 

In this study , the authors present TranAD, a deep transformer network-based anomaly detection and diagnosis model that leverages attention-based sequence encoders to quickly execute inference while being aware of the more general temporal patterns in the data. TranAD employs adversarial training to achieve stability and focus score-based self-conditioning to enable robust multi-modal feature extraction. The paper mentions extensive empirical experiments on six publicly accessible datasets show that TranAD can perform better in detection and diagnosis than state-of-the-art baseline methods with data- and time-efficient training. 

  • Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding 

In the last few years, generative models called “diffusion models” have been increasingly popular. This year saw these models capture the excitement of AI enthusiasts around the world. 

Going ahead of the current text to speech technology of recent times, this outstanding 2022 ML paper introduced the viral text-to-image diffusion model from Google, Imagen. This diffusion model achieves a new state-of-the-art FID score of 7.27 on the COCO dataset by combining the deep language understanding of transformer-based large language models with the photorealistic image-generating capabilities of diffusion models. A text-only frozen language model provides the text representation, and a diffusion model with two super-resolution upsampling stages, up to 1024×2014, produces the images. It employs several training approaches, including classifier-free guiding, to teach itself conditional and unconditional generation. Another important feature of Imagen is the use of dynamic thresholding, which stops the diffusion process from being saturated in specific areas of the picture, a behavior that reduces image quality, particularly when the weight placed on text conditional creation is large.

  • No Language Left Behind: Scaling Human-Centered Machine Translation

This ML paper introduced the most popular Meta projects of the year 2022: NLLB-200. This paper talks about how Meta built and open-sourced this state-of-the-art AI model at FAIR, which is capable of translating 200 languages between each other. It covers every aspect of this technology: language analysis, moral issues, effect analysis, and benchmarking.

No matter what language a person speaks, accessibility via language ensures that everyone can benefit from the growth of technology. Meta claims that several languages that NLLB-200 translates, such as Kamba and Lao, are not currently supported by any translation systems in use. The tech behemoth also created a dataset called “FLORES-200” to evaluate the effectiveness of the NLLB-200 and show that accurate translations are offered. According to Meta, NLLB-200 offers an average of 44% higher-quality translations than its prior model.

  • A Generalist Agent

AI pundits believe that multimodality will play a huge role in the future of Artificial General Intelligence (AGI). One of the most talked ML papers of 2022 by DeepMind introduces Gato – a generalist agent . This AGI agent is a multi-modal, multi-task, multi-embodiment network, which means that the same neural network (i.e. a single architecture with a single set of weights) can do all tasks while integrating inherently diverse types of inputs and outputs. 

DeepMind claims that the general agent can be improved with new data to perform even better on a wider range of tasks. They argue that having a general-purpose agent reduces the need for hand-crafting policy models for each region, enhances the volume and diversity of training data, and enables continuous advances in the data, computing, and model scales. A general-purpose agent can also be viewed as the first step toward artificial general intelligence, which is the ultimate goal of AGI. 

Gato demonstrates the versatility of transformer-based machine learning architectures by exhibiting their use in a variety of applications.  Unlike previous neural network systems tailored for playing games, stack blocks with a real robot arm, read words, and caption images, Gato is versatile enough to perform all of these tasks on its own, using only a single set of weights and a relatively simple architecture.

  • The Forward-Forward Algorithm: Some Preliminary Investigations 

AI pioneer Geoffrey Hinton is known for writing paper on the first deep convolutional neural network and backpropagation. In his latest paper presented at NeurIPS 2022, Hinton proposed the “forward-forward algorithm,” a new learning algorithm for artificial neural networks based on our understanding of neural activations in the brain. This approach draws inspiration from Boltzmann machines (Hinton and Sejnowski, 1986) and noise contrast estimation (Gutmann and Hyvärinen, 2010). According to Hinton, forward-forward, which is still in its experimental stages, can substitute the forward and backward passes of backpropagation with two forward passes, one with positive data and the other with negative data that the network itself could generate. Further, the algorithm could simulate hardware more efficiently and provide a better explanation for the brain’s cortical learning process.

Without employing complicated regularizers, the algorithm obtained a 1.4 percent test error rate on the MNIST dataset in an empirical study, proving that it is just as effective as backpropagation.

The paper also suggests a novel “mortal computing” model that can enable the forward-forward algorithm and understand our brain’s energy-efficient processes.

  • Focal Modulation Networks

In humans, the ciliary muscles alter the shape of the eye and hence the radius of the curvature lens to focus on near or distant objects. Changing the shape of the eye lens, changes the focal length of the lens. Mimicking this behavior of focal modulation in computer vision systems can be tricky.

This machine learning paper introduces FocalNet, an iterative information extraction technique that employs the premise of foveal attention to post-process Deep Neural Network (DNN) outputs by performing variable input/feature space sampling. Its attention-free design outperforms SoTA self-attention (SA) techniques in a wide range of visual benchmarks. According to the paper, focal modulation consists of three parts: According to the paper, focal modulation consists of three parts: 

a. hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from close-up to a great distance; 

b. gated aggregation to selectively gather contexts for each query token based on its content; and  

c. element-wise modulation or affine modification to inject the gathered context into the query.

  • Learning inverse folding from millions of predicted structures

The field of structural biology is being fundamentally changed by cutting-edge technologies in machine learning, protein structure prediction, and innovative ultrafast structural aligners. Time and money are no longer obstacles to obtaining precise protein models and extensively annotating their functionalities. However, determining a protein sequence from its backbone atom coordinates remained a challenge for scientists. To date, machine learning methods to this challenge have been constrained by the amount of empirically determined protein structures available.

In this ICML Outstanding Paper (Runner Up) , authors explain tackling this problem by increasing training data by almost three orders of magnitude by using AlphaFold2 to predict structures for 12 million protein sequences. With the use of this additional data, a sequence-to-sequence transformer with invariant geometric input processing layers is able to recover native sequence on structurally held-out backbones in 51% of cases while recovering buried residues in 72% of cases. This is an improvement of over 10% over previous techniques. In addition to designing protein complexes, partly masked structures, binding interfaces, and numerous states, the concept generalises to a range of other more difficult tasks.

  • MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge

Within the AI research community, using video games as a training medium for AI has gained popularity. These autonomous agents have had great success in Atari games, Starcraft, Dota, and Go. Although these developments have gained popularity in the field of artificial intelligence research, the agents do not generalize beyond a narrow range of activities, in contrast to humans, who continually learn from open-ended tasks.

This thought-provoking 2022 ML paper suggests MineDojo, a unique framework for embodied agent research based on the well-known game Minecraft. In addition to building an internet-scale information base with Minecraft videos, tutorials, wiki pages, and forum discussions, Minecraft provides a simulation suite with tens of thousands of open-ended activities. Using MineDojo data, the author proposes a unique agent learning methodology that employs massive pre-trained video-language models as a learnt reward function. Without requiring a dense shaping reward that has been explicitly created, MinoDojo autonomous agent can perform a wide range of open-ended tasks that are stated in free-form language.

  • Is Out-of-Distribution Detection Learnable?

Machine learning (supervised ML) models are frequently trained using the closed-world assumption, which assumes that the distribution of the testing data will resemble that of the training data. This assumption doesn’t hold true when used in real-world activities, which causes a considerable decline in their performance. While this performance loss is acceptable for applications like product recommendations, developing an out-of-distribution (OOD) identification algorithm is crucial to preventing ML systems from making inaccurate predictions in situations where data distribution in real-world activities typically drifts over time (self-driving cars).

In this paper , authors explore the probably approximately correct (PAC) learning theory of OOD detection, which is proposed by researchers as an open problem, to study the applicability of OOD detection. They first focus on identifying a prerequisite for OOD detection’s learnability. Following that, they attempt to show a number of impossibility theorems regarding the learnability of OOD detection in a handful yet different scenarios.

  • Gradient Descent: The Ultimate Optimizer 

Gradient descent is a popular optimization approach for training machine learning models and neural networks. The ultimate aim of any machine learning (neural network) method is to optimize parameters, but selecting the ideal step size for an optimizer is difficult since it entails lengthy and error-prone manual work. Many strategies exist for automated hyperparameter optimization; however, they often incorporate additional hyperparameters to govern the hyperparameter optimization process. In this study , MIT CSAIL and Meta researchers offer a unique approach that allows gradient descent optimizers like SGD and Adam to tweak their hyperparameters automatically.

They propose learning the hyperparameters by self-using gradient descent, as well as learning the hyper-hyperparameters via gradient descent, and so on indefinitely. This paper describes an efficient approach for allowing gradient descent optimizers to autonomously adjust their own hyperparameters, which may be layered recursively to many levels. As these gradient-based optimizer towers expand in size, they become substantially less sensitive to the selection of top-level hyperparameters, reducing the load on the user to search for optimal values.

  • ProcTHOR: Large-Scale Embodied AI Using Procedural Generation 

Embodied AI is a developing study field that has been influenced by recent advancements in artificial intelligence, machine learning, and computer vision. This method of computer learning makes an effort to translate this connection to artificial systems. The paper proposes ProcTHOR, a framework for procedural generation of Embodied AI environments. ProcTHOR allows researchers to sample arbitrarily huge datasets of diverse, interactive, customisable, and performant virtual environments in order to train and assess embodied agents across navigation, interaction, and manipulation tasks. 

According to the authors, models trained on ProcTHOR using only RGB images and without any explicit mapping or human task supervision achieve cutting-edge results in 6 embodied AI benchmarks for navigation, rearrangement, and arm manipulation, including the ongoing Habitat2022, AI2-THOR Rearrangement2022, and RoboTHOR challenges. The paper received the Outstanding Paper award at NeurIPS 2022.

  • A Commonsense Knowledge Enhanced Network with Retrospective Loss for Emotion Recognition in Spoken Dialog

Emotion Recognition in Spoken Dialog (ERSD) has recently attracted a lot of attention due to the growth of open conversational data. This is due to the fact that excellent speech recognition algorithms have emerged as a result of the integration of emotional states in intelligent spoken human-computer interactions. Additionally, it has been demonstrated that recognizing emotions makes it possible to track the development of human-computer interactions, allowing for dynamic change of conversational strategies and impacting the result (e.g., customer feedback). But the volume of the current ERSD datasets restricts the model’s development. 

This ML paper proposes a Commonsense Knowledge Enhanced Network (CKE-Net) with a retrospective loss to carry out dialog modeling, external knowledge integration, and historical state retrospect hierarchically. 

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Authors with further questions on double-blind reviewing are encouraged to contact the program chairs by email. Papers that do not comply with the double-blind review process will be desk-rejected.

To prevent double submissions, the chairs might compare the submissions with related conferences that have overlapping review periods. The double submission restriction applies only to refereed journals and conferences, not to unrefereed forums (e.g. arXiv.org ). To check for plagiarism issues, the chairs might use external plagiarism detection software.

To facilitate double-blind reviewing, we advise the authors to postpone publishing their submitted work on arXiv or similar sites until after the notification of acceptance . However, if the authors have already published a version of their paper to arXiv or similar sites, we request authors to use a different title for their submission, so that author names are not inadvertently disclosed, e.g., via a notification on Google Scholar.

All publications are subject to the ACM Author Representations policy .

As a published ACM author, you and your co-authors are subject to all ACM Publications Policies , including ACM’s new Publications Policy on Research Involving Human Participants and Subjects .

Important Dates

All dates are 23:59:59 AoE (UTC-12h)

  • Paper registration: 10 March 2022 (to register a paper, only a paper title, an author list and some additional metadata are required)
  • Full paper submission: 17 March 2022
  • 1st Rebuttal period (all papers): 9-13 May, 2022
  • 2nd Additional short response period (selected papers): 30-31 May, 2022
  • Author notification: 14 June 2022
  • Camera-ready submission: 5 Sep 2022

Open Science Policy

The research track of ESEC/FSE has introduced an open science policy. Openness in science is key to fostering scientific progress via transparency, reproducibility, and replicability. The steering principle is that all research results should be accessible to the public, if possible, and that empirical studies should be reproducible. In particular, we actively support the adoption of open data and open source principles and encourage all contributing authors to disclose (anonymized and curated) data to increase reproducibility and replicability.

Upon submission to the research track, authors are asked to make their data available to the program committee (via upload of anonymized supplemental material or a link to an anonymized private or public repository) or to comment on why this is not possible or desirable. While sharing such a repository is not mandatory for submission or acceptance, this information will be passed to the program committee to inform its decision. Furthermore, authors are asked to indicate whether they intend to make their data publicly available upon acceptance. For more details on ESEC/FSE open science policy, please refer to the official guidelines.

Authors of accepted papers will be given an opportunity (and encouragement) to submit their data and tools to the separate ESEC/FSE’21 artifact evaluation committee.

Topics of Interest

Topics of interest include, but are not limited to:

  • Artificial intelligence and machine learning for software engineering
  • Autonomic computing
  • Debugging and fault localization
  • Dependability, safety, and reliability
  • Distributed and collaborative software engineering
  • Embedded software, safety-critical systems, and cyber-physical systems
  • Empirical software engineering
  • Formal methods
  • Human-computer interaction
  • Mining software repositories
  • Mobile development
  • Model checking
  • Model-driven engineering
  • Parallel, distributed, and concurrent systems
  • Performance engineering
  • Program analysis
  • Program comprehension
  • Program repair
  • Program synthesis
  • Programming languages
  • Recommendation systems
  • Requirements engineering
  • Search-based software engineering
  • Services, components, and cloud
  • Software architectures
  • Software engineering education
  • Software engineering for machine learning and artificial intelligence
  • Software evolution
  • Software processes
  • Software security
  • Software testing
  • Software traceability
  • Symbolic execution
  • Tools and environments

Program Display Configuration

Mon 14 nov displayed time zone: beijing, chongqing, hong kong, urumqi change, tue 15 nov displayed time zone: beijing, chongqing, hong kong, urumqi change, wed 16 nov displayed time zone: beijing, chongqing, hong kong, urumqi change, not scheduled yet, faq on review process and double-anonymous reviewing.

Q: What is the ESEC/FSE 2021 open science policy and how can I follow it?

Openness in science is key to fostering scientific progress via transparency, reproducibility, and replicability. Upon submission to the research track, authors are asked to:

  • make their data available to the program committee (via upload of supplemental material or a link to an anonymous repository) and provide instructions on how to access this data in the paper; or
  • include in the paper an explanation as to why this is not possible or desirable; and
  • indicate if they intend to make their data publicly available upon acceptance.

Q: How can I upload supplementary material via the HotCRP site and make it anonymous for double-anonymous review?

To conform to the double-anonymous policy , please include an anonymized URL. Code and data repositories may be exported to remove version control history, scrubbed of names in comments and metadata, and anonymously uploaded to a sharing site.

Rationale for Double-Anonymous Reviewing (DAR)

Q: Why are you using double-anonymous reviewing?

A: Studies have shown that a reviewer’s attitude toward a submission may be affected, even unconsciously, by the identity of the authors.

Q: Do you really think DAR actually works? I suspect reviewers can often guess who the authors are anyway.

A: It is rare for authorship to be guessed correctly, even by expert reviewers, as detailed in this study .

For Authors

Q: What exactly do I have to do to anonymize my paper?

A: Use common sense. Your job is not to make your identity undiscoverable but simply to make it possible for reviewers to evaluate your submission without having to know who you are: omit authors’ names from your title page, and when you cite your own work, refer to it in the third person. Also, be sure not to include any acknowledgements that would give away your identity. You should also avoid revealing the institutional affiliation of authors

Q: I would like to provide supplementary material for consideration, e.g., the code of my implementation or proofs of theorems. How do I do this?

On the submission site, there will be an option to submit supplementary material along with your main paper. This supplementary material should also be anonymized; it may be viewed by reviewers during the review period, so it should adhere to the same double-blind guidelines.

Q: My submission is based on code available in a public repository. How do I deal with this?

A: Making your code publicly available is not incompatible with double-blind reviewing. You should do the following. You can cite the code in your paper, but remove the actual URL. Instead create an anonymized version of the repository and include a new URL that points to the anonymized version of the repository, similar to how you would include supplementary materials to adhere to the Open Science policy.

Q: I am building on my own past work on the WizWoz system. Do I need to rename this system in my paper for purposes of anonymity, so as to remove the implied connection between my authorship of past work on this system and my present submission?

A: Maybe. The core question is really whether the system is one that, once identified, automatically identifies the author(s) and/or the institution. If the system is widely available, and especially if it has a substantial body of contributors and has been out for a while, then these conditions may not hold (e.g., LLVM or HotSpot), because there would be considerable doubt about authorship. By contrast, a paper on a modification to a proprietary system (e.g., Visual C++, or a research project that has not open-sourced its code) implicitly reveals the identity of the authors or their institution. If naming your system essentially reveals your identity (or institution), then anonymize it. In your submission, point out that the system name has been anonymized. If you have any doubts, please contact the Program Chair.

Q: I am submitting a paper that extends my own work that previously appeared at a workshop. Should I anonymize any reference to that prior work?

A: No. But we recommend you do not use the same title for your ESEC/FSE submission, so that it is clearly distinguished from the prior paper. In general, there is rarely a good reason to anonymize a citation. When in doubt, contact the Program Co-Chairs.

Q: Am I allowed to post my (non-blinded) paper on my web page or arXiv?

A: If the authors have already published a version of their paper to arXiv or similar sites, we request authors to consider using a different title for their submission, so that author names are not inadvertently disclosed, e.g., via a notification on Google Scholar. Another option would be to postpone publishing their submitted work on arXiv or similar sites, until after the notification of acceptance. If the paper is accepted, you may request a title change to the PC chairs (via email to [email protected] , with subject “ESEC/FSE 2022 [PAPER-ID]: Title change request”), which will be subject to reviewers’ approval.

Q: Can I give a talk about my work while it is under review? How do I handle social media?

A: We have developed guidelines, described here, to help everyone navigate in the same way the tension between the normal communication of scientific results, which double-anonymous reviewing should not impede, and actions that essentially force potential reviewers to learn the identity of the authors for a submission. Roughly speaking, you may (of course!) discuss work under submission, but you should not broadly advertise your work through media that is likely to reach your reviewers. We acknowledge there are gray areas and trade-offs; we cannot describe every possible scenario.

Things you may do:

  • Put your submission on your home page.
  • Discuss your work with anyone who is not on the review committees, or with people on the committees with whom you already have a conflict.
  • Present your work at professional meetings, job interviews, etc.
  • Submit work previously discussed at an informal workshop, previously posted on arXiv or a similar site, previously submitted to a conference not using double-anonymous reviewing, etc.

Things you should not do:

  • Contact members of the review committees about your work, or deliberately present your work where you expect them to be.
  • Publicize your work on major mailing lists used by the community (because potential reviewers likely read these lists).
  • Publicize your work on social media if wide public [re-]propagation is common (e.g., Twitter) and therefore likely to reach potential reviewers. For example, on Facebook, a post with a broad privacy setting (public or all friends) saying, “Whew, ESEC/FSE paper in, time to sleep” is okay, but one describing the work or giving its title is not appropriate. Alternatively, a post to a group including only the colleagues at your institution is fine.

Reviewers will not be asked to recuse themselves from reviewing your paper unless they feel you have gone out of your way to advertise your authorship information to them. If you are unsure about what constitutes “going out of your way”, please contact the Program Co-Chairs.

Q: Will the fact that ESEC/FSE is double-anonymous have an impact on handling conflicts of interest?

A: Double-anonymous reviewing does not change the principle that reviewers should not review papers with which they have a conflict of interest, even if they do not immediately know who the authors are. Authors declare conflicts of interest when submitting their papers using the guidelines in the Cal for Papers. Papers will not be assigned to reviewers who have a conflict. Note that you should not declare gratuitous conflicts of interest and the chairs will compare the conflicts declared by the authors with those declared by the reviewers. Papers abusing the system will be desk-rejected.

For Reviewers

Q: What should I do if I learn the authors’ identity? What should I do if a prospective ESEC/FSE author contacts me and asks to visit my institution?

A: If you feel that the authors’ actions are largely aimed at ensuring that potential reviewers know their identity, contact the Program Chairs. Otherwise, you should not treat double-anonymous reviewing differently from other reviewing. In particular, refrain from seeking out information on the authors’ identity, but if you discover it accidentally this will not automatically disqualify you as a reviewer. Use your best judgment.

Q: The authors have provided a URL to supplemental material. I would like to see the material but I worry they will snoop my IP address and learn my identity. What should I do?

A: Contact the Program Chairs, who will download the material on your behalf and make it available to you.

Q: If I am assigned a paper for which I feel I am not an expert, how do I seek an outside review?

A: PC members should do their own reviews, not delegate them to someone else. Please contact the Program Chairs, especially since additional reviewers might have a different set of conflicts of interest.

Q: How do we handle potential conflicts of interest since I cannot see the author names?

A: The conference review system will ask that you identify conflicts of interest when you get an account on the submission system.

Q: How should I avoid learning the authors’ identity, if I am using web-search in the process of performing my review?

A: You should make a good-faith effort not to find the authors’ identity during the review period, but if you inadvertently do so, this does not disqualify you from reviewing the paper. As part of the good-faith effort, please turn off Google Scholar auto-notifications. Please do not use search engines with terms like the paper’s title or the name of a new system being discussed. If you need to search for related work you believe exists, do so after completing a preliminary review of the paper.

Review Process

Q: Can I withdraw my paper?

A: Yes, papers can be withdrawn at any time using HotCRP.

Cristian Cadar

Cristian Cadar Program Co-Chair

Imperial college london, uk, united kingdom.

Miryung Kim

Miryung Kim Program Co-Chair

University of california at los angeles, usa, united states.

Aldeida Aleti

Aldeida Aleti

Monash university.

Muhammad Ali Gulzar

Muhammad Ali Gulzar

Virginia tech, usa.

Dalal Alrajeh

Dalal Alrajeh

Imperial college london.

Sébastien Bardin

Sébastien Bardin

Cea list, university paris-saclay.

Earl T. Barr

Earl T. Barr

University college london.

Jonathan Bell

Jonathan Bell

Northeastern university.

Antonia Bertolino

Antonia Bertolino

National research council, italy.

Árpád Beszédes

Árpád Beszédes

Department of software engineering, university of szeged.

Dirk Beyer

Christian Bird

Microsoft research.

Kelly Blincoe

Kelly Blincoe

University of auckland, new zealand.

Eric Bodden

Eric Bodden

Heinz nixdorf institut, paderborn university and fraunhofer iem.

Ray Buse

Marcel Böhme

Mpi-sp, germany and monash university, australia.

Yan Cai

Institute of Software at Chinese Academy of Sciences

Antonio Carzaniga

Antonio Carzaniga

Università della svizzera italiana, switzerland.

Satish Chandra

Satish Chandra

Meta platforms.

micro-avatar

Sudipta Chattopadhyay

Singapore university of technology and design.

Marsha Chechik

Marsha Chechik

University of toronto.

Junjie Chen

Junjie Chen

Tianjin university.

Maria Christakis

Maria Christakis

Myra Cohen

Iowa State University

Eva Darulova

Eva Darulova

Uppsala university.

Robert DeLine

Robert DeLine

Giovanni Denaro

Giovanni Denaro

University of milano-bicocca, italy.

Elisabetta Di Nitto

Elisabetta Di Nitto

Politecnico di milano.

Massimiliano Di Penta

Massimiliano Di Penta

University of sannio, italy.

Antonio Filieri

Antonio Filieri

Aws and imperial college london.

Gordon Fraser

Gordon Fraser

University of passau.

Alessandro Garcia

Alessandro Garcia

Georgios Gousios

Georgios Gousios

Endor labs & delft university of technology, netherlands.

Jin L.C. Guo

Jin L.C. Guo

Mcgill university.

Dan Hao

Peking University

Mark Harman

Mark Harman

Fei He

Tsinghua University

Anindya iqbal, bangladesh university of engineering and technology dhaka, bangladesh, franjo ivancic.

Reyhaneh Jabbarvand

Reyhaneh Jabbarvand

University of illinois at urbana-champaign.

Ciera Jaspan

Ciera Jaspan

Lingxiao Jiang

Lingxiao Jiang

Singapore management university.

Christine Julien

Christine Julien

University of texas at austin.

Gail Kaiser

Gail Kaiser

Columbia university.

Akash Lal

Owolabi Legunsen

Cornell university.

Crista Lopes

Crista Lopes

University of california, irvine.

Shan Lu

University of Chicago

Michael Lyu

Michael Lyu

Chinese university of hong kong.

Jie M. Zhang

Jie M. Zhang

King's college london.

Lei Ma

University of Alberta

Patrícia Machado

Patrícia Machado

Federal university of campina grande.

Sam Malek

University of California at Irvine

Paul Marinescu

Paul Marinescu

Darko Marinov

Darko Marinov

Phil McMinn

Phil McMinn

University of sheffield.

Mira Mezini

Mira Mezini

Tu darmstadt.

Nicole Novielli

Nicole Novielli

University of bari.

Alessandro Orso

Alessandro Orso

Georgia tech.

Ipek Ozkaya

Ipek Ozkaya

Carnegie mellon university.

Rohan Padhye

Rohan Padhye

Mike Papadakis

Mike Papadakis

University of luxembourg, luxembourg.

Liliana Pasquale

Liliana Pasquale

University college dublin & lero.

Xin Peng

Fudan University

Justyna Petke

Justyna Petke

Luís Pina

University of Illinois at Chicago

Martin Pinzger

Martin Pinzger

Alpen-adria-universität klagenfurt.

Pavithra Prabhakar

Pavithra Prabhakar

Kansas state university.

Michael Pradel

Michael Pradel

University of stuttgart.

Rahul Purandare

Rahul Purandare

Corina S. Păsăreanu

Corina S. Păsăreanu

Ajitha Rajan

Ajitha Rajan

University of edinburgh.

Baishakhi Ray

Baishakhi Ray

Manuel Rigger

Manuel Rigger

National university of singapore.

Noam Rinetzky

Noam Rinetzky

Tel aviv university.

Romain Robbes

Romain Robbes

Free university of bozen-bolzano.

Gregorio Robles

Gregorio Robles

Universidad rey juan carlos, paige rodeghero, clemson university.

Cindy Rubio-González

Cindy Rubio-González

University of california at davis.

Neha Rungta

Neha Rungta

Amazon web services.

Alessandra Russo

Alessandra Russo

Sukyoung Ryu

Sukyoung Ryu

South korea.

Indranil Saha

Indranil Saha

Federica Sarro

Federica Sarro

Bonita Sharif

Bonita Sharif

University of nebraska-lincoln, usa.

Elena Sherman

Elena Sherman

Boise state university.

Diomidis Spinellis

Diomidis Spinellis

Athens university of economics and business; delft university of technology.

Zhendong Su

Zhendong Su

Yulei Sui

University of New South Wales

Chengnian Sun

Chengnian Sun

University of waterloo.

Lin Tan

Purdue University

Shin Hwei Tan

Shin Hwei Tan

Southern university of science and technology.

Paolo Tonella

Paolo Tonella

Christoph Treude

Christoph Treude

University of melbourne.

Omer Tripp

Rachel Tzoref-Brill

Ibm research.

Sebastian Uchitel

Sebastian Uchitel

Universidad de buenos aires / imperial college.

Willem Visser

Willem Visser

Stellenbosch university, south africa.

Chao Wang

Huazhong University of Science and Technology

Danny Weyns

Danny Weyns

Laurie Williams

Laurie Williams

North carolina state university.

Zhenchang Xing

Zhenchang Xing

Csiro’s data61; australian national university.

Yingfei Xiong

Yingfei Xiong

Institute of software at chinese academy of sciences; university of chinese academy of sciences.

Tuba Yavuz

University of Florida

Jooyong Yi

UNIST (Ulsan National Institute of Science and Technology)

Shin Yoo

Tingting Yu

University of cincinnati.

Andy Zaidman

Andy Zaidman

Delft university of technology.

Hongyu Zhang

Hongyu Zhang

University of newcastle.

Lingming Zhang

Lingming Zhang

Tianyi Zhang

Tianyi Zhang

Xiangyu Zhang

Xiangyu Zhang

Xi Zheng

Macquarie University

Minghui Zhou

Minghui Zhou

Shurui Zhou

Shurui Zhou

Andrea Zisman

Andrea Zisman

The open university.

Zhiqiang Zuo

Zhiqiang Zuo

Nanjing university.

Marcelo d'Amorim

Marcelo d'Amorim

North carolina state university, united states / federal university of pernambuco, brazil.

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The Top 17 ‘Must-Read’ AI Papers in 2022

The Top 17 ‘Must-Read’ AI Papers in 2022

We caught up with experts in the RE•WORK community to find out what the top 17 AI papers are for 2022 so far that you can add to your Summer must reads. The papers cover a wide range of topics including AI in social media and how AI can benefit humanity and are free to access.

Interested in learning more? Check out all the upcoming RE•WORK events to find out about the latest trends and industry updates in AI here .

Max Li, Staff Data Scientist – Tech Lead at Wish

Max is a Staff Data Scientist at Wish where he focuses on experimentation (A/B testing) and machine learning.  His passion is to empower data-driven decision-making through the rigorous use of data. View Max’s presentation, ‘Assign Experiment Variants at Scale in A/B Tests’, from our Deep Learning Summit in February 2022 here .

1. Boostrapped Meta-Learning (2022) – Sebastian Flennerhag et al.

The first paper selected by Max proposes an algorithm in which allows the meta-learner teach itself, allowing to overcome the meta-optimisation challenge. The algorithm focuses meta-learning with gradients, which guarantees improvements in performance. The paper also looks at how bootstrapping opens up possibilities. Read the full paper here .

2. Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces (2022) – Samuel Daulton et al.

Another paper selected by Max proposes MORBO, a scalable method for multiple-objective BO as it performs better than that of high-dimensional search spaces. MORBO significantly improves the sample efficiency, and where BO algorithms fail, MORBO provides improved sample efficiencies to the current BO approach used. Read the full paper here .

3. Tabular Data: Deep Learning is Not All You Need (2021) – Ravid Shwartz-Ziv, Amitai Armon

To solve real-life data science problems, selecting the right model to use is crucial. This final paper selected by Max explores whether deep models should be recommended as an option for tabular data. Read the full paper here .

research paper of 2022

Jigyasa Grover, Senior Machine Learning Engineer at Twitter

Jigyasa Grover is a Senior Machine Learning Engineer at Twitter working in the performance ads ranking domain. Recently, she was honoured with the 'Outstanding in AI: Young Role Model Award' by Women in AI across North America. She is one of the few ML Google Developer Experts globally. Jigyasa has previously presented at our Deep Learning Summit and MLOps event in San Fransisco earlier this year.

4. Privacy for Free: How does Dataset Condensation Help Privacy? (2022) – Tian Dong et al.

Jigyasa’s first recommendation concentrates on Privacy Preserving Machine Learning, specifically mitigating the leakage of sensitive data in Machine Learning. The paper provides one of the first propositions of using dataset condensation techniques to preserve the data efficiency during model training and furnish membership privacy. This paper was published by Sony AI and won the Outstanding Paper Award at ICML 2022. Read the full paper here .

5. Affective Signals in a Social Media Recommender System (2022) – Jane Dwivedi-Yu et al.

The second paper recommended by Jigyasa talks about operationalising Affective Computing, also known as Emotional AI, for an improved personalised feed on social media. The paper discusses the design of an affective taxonomy customised to user needs on social media. It further lays out the curation of suitable training data by combining engagement data and data from a human-labelling task to enable the identification of the affective response a user might exhibit for a particular post. Read the full paper here .

6. ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest (2022) – Paul Baltescu et al.

Jigyasa’s last recommendation is a paper by Pinterest that illustrates the aggregation of both textual and visual information to build a unified set of product embeddings to enhance recommendation results on e-commerce websites. By applying multi-task learning, the proposed embeddings can optimise for multiple engagement types and ensures that the shopping recommendation stack is efficient with respect to all objectives. Read the full article here .

Asmita Poddar, Software Development Engineer at Amazon Alexa

Asmita is a Software Development Engineer at Amazon Alexa, where she works on developing and productionising natural language processing and speech models. Asmita also has prior experience in applying machine learning in diverse domains. Asmita will be presenting at our London AI Summit , in September, where she will discuss AI for Spoken Communication.

7. Competition-Level Code Generation with AlphaCode (2022) – Yujia Li et al.

Systems can help programmers become more productive. Asmita has selected this paper which addresses the problems with incorporating innovations in AI into these systems. AlphaCode is a system that creates solutions for problems that requires deeper reasoning. Read the full paper here .

8. A Commonsense Knowledge Enhanced Network with Retrospective Loss for Emotion Recognition in Spoken Dialog (2022) – Yunhe Xie et al.

There are limits to model’s reasoning in regards to the existing ERSD datasets. The final paper selected by Asmita proposes a Commonsense Knowledge Enhanced Network with a backward-looking loss to perform dialog modelling, external knowledge integration and historical state retrospect. The model used has been shown to outperform other models. Read the full paper here .

research paper of 2022

Discover the speakers we have lined up and the topics we will cover at the London AI Summit.

Sergei Bobrovskyi, Expert in Anomaly Detection for Root Cause Analysis at Airbus

Dr. Sergei Bobrovskyi is a Data Scientist within the Analytics Accelerator team of the Airbus Digital Transformation Office. His work focuses on applications of AI for anomaly detection in time series, spanning various use-cases across Airbus. Sergei will be presenting at our Berlin AI Summit in October about Anomaly Detection, Root Cause Analysis and Explainability.

9. LaMDA: Language Models for Dialog Applications (2022) – Romal Thoppilan et al.

The paper chosen by Sergei describes the LaMDA system, which caused the furor this summer, when a former Google engineer claimed it has shown signs of being sentient. LaMDA is a family of large language models for dialog applications based on Transformer architecture. The interesting feature of the model is their fine-tuning with human annotated data and possibility to consult external sources. In any case, this is a very interesting model family, which we might encounter in many of the applications we use daily. Read the full paper here .

10. A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27 (2022) – Yann LeCun

The second paper chosen by Sergei provides a vision on how to progress towards general AI. The study combines a number of concepts including configurable predictive world model, behaviour driven through intrinsic motivation, and hierarchical joint embedding architectures. Read the full paper here .

11. Coordination Among Neural Modules Through a Shared Global Workpace (2022) – Anirudh Goyal et al.

This paper chosen by Sergei combines the Transformer architecture underlying most of the recent successes of deep learning with ideas from the Global Workspace Theory from cognitive sciences. This is an interesting read to broaden the understanding of why certain model architectures perform well and in which direction we might go in the future to further improve performance on challenging tasks. Read the full paper here .

12. Magnetic control of tokamak plasmas through deep reinforcement learning (2022) – Jonas Degrave et al.

Sergei chose the next paper, which asks the question of ‘how can AI research benefit humanity?’. The use of AI to enable safe, reliable and scalable deployment of fusion energy could contribute to the solution of pression problems of climate change. Sergei has said that this is an extremely interesting application of AI technology for engineering. Read the full paper here .

13. TranAd: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data (2022) – Shreshth Tuli, Giuliano Casale and Nicholas R. Jennings

The final paper chosen by Sergei is a specialised paper applying transformer architecture to the problem of unsupervised anomaly detection in multivariate time-series. Many architectures which were successful in other fields are at some points being also applied to time-series. The paper shows an improved performance on some known data sets. Read the full paper here .

research paper of 2022

Abdullahi Adamu, Senior Software Engineer at Sony

Abdullahi has worked in various industries including working at a market research start-up where he developed models that could extract insights from human conversations about products or services. He moved to Publicis, where he became Data Engineer and Data Scientist in 2018. Abdullahi will be part of our panel discussion at the London AI Summit in September, where he will discuss Harnessing the Power of Deep Learning.

14. Self-Supervision for Learning from the Bottom Up (2022) – Alexei Efros

This paper chosen by Abdullahi makes compelling arguments for why self-supervision is the next step in the evolution of AI/ML for building more robust models. Overall, these compelling arguments justify even further why self-supervised learning is important on our journey towards more robust models that generalise better in the wild. Read the full paper here .

15. Neural Architecture Search Survey: A Hardware Perspective (2022) – Krishna Teja Chitty-Venkata and Arun K. Somani

Another paper chosen by Abdullahi understands that as we move towards edge computing and federated learning, neural architecture search that takes into account hardware constraints which will be more critical in ensuring that we have leaner neural network models that balance latency and generalisation performance. This survey gives a birds eye view of the various neural architecture search algorithms that take into account hardware constraints to design artificial neural networks that give the best tradeoff of performance and accuracy. Read the full paper here .

16. What Should Not Be Contrastive In Contrastive Learning (2021) – Tete Xiao et al.

In the paper chosen by Abdullahi highlights the underlying assumptions behind data augmentation methods and how these can be counter productive in the context of contrastive learning; for example colour augmentation whilst a downstream task is meant to differentiate colours of objects. The result reported show promising results in the wild. Overall, it presents an elegant solution to using data augmentation for contrastive learning. Read the full paper here .

17. Why do tree-based models still outperform deep learning on tabular data? (2022) – Leo Grinsztajn, Edouard Oyallon and Gael Varoquaux

The final paper selected by Abdulliah works on answering the question of why deep learning models still find it hard to compete on tabular data compared to tree-based models. It is shown that MLP-like architectures are more sensitive to uninformative features in data, compared to their tree-based counterparts. Read the full paper here .

Sign up to the RE•WORK monthly newsletter for the latest AI news, trends and events.

Join us at our upcoming events this year:

·       London AI Summit – 14-15 September 2022

·       Berlin AI Summit – 4-5 October 2022

·       AI in Healthcare Summit Boston – 13-14 October 2022

·       Sydney Deep Learning and Enterprise AI Summits – 17-18 October 2022

·       MLOps Summit – 9-10 November 2022

·       Toronto AI Summit – 9-10 November 2022

·       Nordics AI Summit - 7-8 December 2022

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  • Conferences
  • Last updated April 25, 2022
  • In AI Origins & Evolution

Top 8 research papers by DeepMind in 2022 (till date)

research paper of 2022

  • Published on April 25, 2022
  • by Kartik Wali

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DeepMind’s researchers are working round the clock to push the frontiers of AI. The lab has published 34 research papers in the last four months. Let’s look at the key papers the Alphabet subsidiary has published in 2022.

  • An empirical analysis of compute-optimal large language model training

The paper found the model size and the training dataset size should be scaled in equal measure for compute-optimal training. The researchers tested the theory by training a compute-optimal model, Chinchilla , using the same compute budget as Gopher but with 70B parameters and 4x more data. Chinchilla outperformed Gopher, GPT-3, Jurassic-1, and Megatron-Turing NLG on a slew of downstream evaluation tasks. Chinchilla clocked an average accuracy of 67.5% on the MMLU benchmark, a 7% improvement over Gopher.

research paper of 2022

Source: arxiv.org

  • Restoring and attributing ancient texts using deep neural networks

The paper proposed a deep neural network, Ithaca , for the textual restoration, geographical attribution and chronological attribution of ancient Greek inscriptions. The model focuses on collaboration, decision support and interpretability and clocked 62% accuracy in restoring damaged texts. Historians leveraged Ithaca and saw an accuracy improvement from 25% to 72%. Ithaca can map inscriptions to their original location with 71% accuracy.

research paper of 2022

Source: nature.com

Restorative tools like Ithaca shows how historians can leverage AI to study important periods in human history.

  • Red Teaming Language Models with Language Models

Language models have a tendency to go rogue. Using human annotators to come up with maximum test cases before deployment is expensive The paper outlines how a language model can be used to generate test cases ( red teaming ) to size up the harmful potential of LMs . The researchers evaluated the target LM’s replies to generated test questions using a classifier trained to identify offensive content, surfacing thousands of offensive replies in a 280B parameter LM chatbot. The team explored methods like zero-shot generation and reinforcement learning to generate test cases with varying levels of diversity and difficulty. The paper showed that LM-based red teaming is one promising tool for finding and fixing untoward LM behaviours before deployment.

research paper of 2022

  • Magnetic control of tokamak plasmas through deep reinforcement learning

The paper introduced an architecture for tokamak magnetic controller design that autonomously learns to command the full set of control coils. The research team produced and controlled a varied set of plasma configurations on the Tokamak Configuration Variable. The approach  accurate tracking of the location, current and shape for these configurations. The researchers also demonstrated sustained ‘droplets’ on TCV where two separate plasmas are maintained simultaneously within the vessel, a remarkable advance for tokamak feedback control, showing the potential of reinforcement learning to accelerate research in the fusion domain.

research paper of 2022

  • Competition-Level Code Generation with AlphaCode

The paper introduced Alphacode , a code generation system for solving competitive-level programming problems. The team used large transformer language models to generate code, pre-training them on select GitHub code and fine-tuning on a set of competitive programming problems. AlphaCode achieved an estimated rank within the top 54% of participants in programming competitions.

research paper of 2022

Source: DeepMind

  • MuZero with Self-competition for Rate Control in VP9 Video Compression

The paper proposed an application of the MuZero algorithm to optimise video compression. The team looked into the problem of learning a rate control policy to select the quantization parameters (QP) in the encoding process of libvpx, an open source VP9 video compression library. The researchers treated it as a sequential decision making problem to maximise the video quality with an episodic constraint from the target bitrate. The team introduced a novel self-competition based reward mechanism to solve constrained RL with variable constraint satisfaction difficulty. The MuZero-based rate control achieved an average 6.28% reduction in size of the compressed videos for the same delivered video quality level compared to libvpx’s two-pass VBR rate control policy.

research paper of 2022

  • Learning Robust Real-Time Cultural Transmission without Human Data

The DeepMind team developed a method for generating zero-shot, high recall cultural transmission in AI agents. The agents succeeded at real-time cultural transmission from humans in novel contexts without using any pre-collected human data. The artificial agent was parameterised by a neural network and the team used deep reinforcement learning (RL) to train the weights. The resulting neural network (with fixed weights) is capable of zeroshot, high-recall cultural transmission within a “test” episode on a wide range of unseen tasks. 

research paper of 2022

  • Fair Normalising flows

The paper introduced a new approach, Fair Normalizing Flows (FNF),  providing more rigorous fairness guarantees for learned representations. The main idea is to model the encoder as a normalizing flow trained to minimise the statistical distance between the latent representations of different groups. FNF offers guarantees on the maximum unfairness of any potentially adversarial downstream predictor. The team demonstrated the effectiveness of FNF in enforcing various group fairness notions, interpretability and transfer learning across challenging real-world datasets.

research paper of 2022

Source: openreview.net

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Which social media platforms are most common, who uses each social media platform, find out more, social media fact sheet.

Many Americans use social media to connect with one another, engage with news content, share information and entertain themselves. Explore the patterns and trends shaping the social media landscape.

To better understand Americans’ social media use, Pew Research Center surveyed 5,733 U.S. adults from May 19 to Sept. 5, 2023. Ipsos conducted this National Public Opinion Reference Survey (NPORS) for the Center using address-based sampling and a multimode protocol that included both web and mail. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race and ethnicity, education and other categories.

Polls from 2000 to 2021 were conducted via phone. For more on this mode shift, read our Q&A.

Here are the questions used for this analysis , along with responses, and  its methodology ­­­.

A note on terminology: Our May-September 2023 survey was already in the field when Twitter changed its name to “X.” The terms  Twitter  and  X  are both used in this report to refer to the same platform.

research paper of 2022

YouTube and Facebook are the most-widely used online platforms. About half of U.S. adults say they use Instagram, and smaller shares use sites or apps such as TikTok, LinkedIn, Twitter (X) and BeReal.

Note: The vertical line indicates a change in mode. Polls from 2012-2021 were conducted via phone. In 2023, the poll was conducted via web and mail. For more details on this shift, please read our Q&A . Refer to the topline for more information on how question wording varied over the years. Pre-2018 data is not available for YouTube, Snapchat or WhatsApp; pre-2019 data is not available for Reddit; pre-2021 data is not available for TikTok; pre-2023 data is not available for BeReal. Respondents who did not give an answer are not shown.

Source: Surveys of U.S. adults conducted 2012-2023.

research paper of 2022

Usage of the major online platforms varies by factors such as age, gender and level of formal education.

% of U.S. adults who say they ever use __ by …

  • RACE & ETHNICITY
  • POLITICAL AFFILIATION

research paper of 2022

This fact sheet was compiled by Research Assistant  Olivia Sidoti , with help from Research Analyst  Risa Gelles-Watnick , Research Analyst  Michelle Faverio , Digital Producer  Sara Atske , Associate Information Graphics Designer Kaitlyn Radde and Temporary Researcher  Eugenie Park .

Follow these links for more in-depth analysis of the impact of social media on American life.

  • Americans’ Social Media Use  Jan. 31, 2024
  • Americans’ Use of Mobile Technology and Home Broadband  Jan. 31 2024
  • Q&A: How and why we’re changing the way we study tech adoption  Jan. 31, 2024

Find more reports and blog posts related to  internet and technology .

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ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

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  • Introduction
  • Conclusions
  • Article Information

This histogram plots the overall distribution of surgeons’ preference (probability intraoperative TEE use by surgeon). The blue lines demarcate surgeons with equivocal preference for intraoperative TEE (eg, surgeons with probability of TEE use between 0.30 and 0.70).

Overview of the study design. The left panels illustrate the all-patient, across-hospital, across-surgeon matched comparison. The right panels illustrate the within-hospital, within-surgeon (with equivocal TEE preference: TEE 0.30–0.70) matched comparison.

eAppendix 1. Descriptive Statistics of the Study Cohort

eAppendix 2. Definition of Primary, Secondary, and Negative Control Outcomes

eAppendix 3. Variability in TEE Preference

eAppendix 4. Details on Statistical Matching Methodology

eAppendix 5. Covariate Balance After Matching

eAppendix 6. Additional Details on Outcome Analysis

eAppendix 7. Subgroup Analysis

eAppendix 8. Sensitivity Analysis I: Assessing Robustness of Primary Outcome Findings to Unmeasured Confounding

eAppendix 9. Sensitivity Analysis II: Assessing Robustness of Results to the Missingness of the TEE Status

eAppendix 10. Details On the Negative Control Outcome

eAppendix 11. R and Stata Code

eReferences

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MacKay EJ , Zhang B , Augoustides JG , Groeneveld PW , Desai ND. Association of Intraoperative Transesophageal Echocardiography and Clinical Outcomes After Open Cardiac Valve or Proximal Aortic Surgery. JAMA Netw Open. 2022;5(2):e2147820. doi:10.1001/jamanetworkopen.2021.47820

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Association of Intraoperative Transesophageal Echocardiography and Clinical Outcomes After Open Cardiac Valve or Proximal Aortic Surgery

  • 1 Department of Anesthesiology and Critical Care, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
  • 2 Penn Center for Perioperative Outcomes Research and Transformation (CPORT), University of Pennsylvania, Philadelphia
  • 3 Penn’s Cardiovascular Outcomes, Quality and Evaluative Research Center (CAVOQER), University of Pennsylvania, Philadelphia
  • 4 Leonard Davis Institute of Health Economics (LDI), University of Pennsylvania, Philadelphia
  • 5 Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia
  • 6 Department of Internal Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
  • 7 Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia
  • 8 Division of Cardiovascular Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia

Question   Is intraoperative transesophageal echocardiography (TEE) use associated with improved clinical outcomes among patients undergoing cardiac valve or proximal aortic surgery?

Findings   This matched cohort study of 872 936 patients undergoing cardiac valve or aortic surgery between 2011 and 2019 found that intraoperative TEE use was associated with lower 30-day mortality, a lower incidence of stroke or 30-day mortality, and a lower incidence of cardiac reoperation or 30-day mortality.

Meaning   These findings suggest that intraoperative TEE may improve clinical outcomes after open cardiac valve (repair or replacement) and/or aortic surgery.

Importance   Intraoperative transesophageal echocardiography (TEE) is used frequently in cardiac valve and proximal aortic surgical procedures, but there is a lack of evidence associating TEE use with improved clinical outcomes.

Objective   To test the association between intraoperative TEE use and clinical outcomes following cardiac valve or proximal aortic surgery.

Design, Setting, and Participants   This matched, retrospective cohort study used national registry data from the Society of Thoracic Surgeon (STS) Adult Cardiac Surgery Database (ACSD) to compare clinical outcomes among patients undergoing cardiac valve or proximal aortic surgery with vs without intraoperative TEE. Statistical analyses used optimal matching within propensity score calipers to conduct multiple matched comparisons including within-hospital and within-surgeon matches, a negative control outcome analysis, and sensitivity analyses. STS ACSD data encompasses more than 90% of all hospitals that perform cardiac surgery in the US. The study cohort consisted of all patients aged at least 18 years undergoing open cardiac valve repair or replacement surgery and/or proximal aortic surgery between 2011 and 2019. Statistical analysis was performed from October 2020 to April 2021.

Exposures   The exposure was receipt of intraoperative TEE during the cardiac valve or proximal aortic surgery.

Main Outcomes and Measures   The primary outcome was death within 30 days of surgery. The secondary outcomes were (1) a composite outcome of stroke or 30-day mortality and (2) a composite outcome of reoperation or 30-day mortality.

Results   Of the 872 936 patients undergoing valve or aortic surgery, 540 229 (61.89%) were male; 63 565 (7.28%) were Black and 742 384 (85.04%) were White; 711 326 (81.5%) received TEE and 161 610 (18.5%) did not receive TEE; the mean (SD) age was 65.61 years (13.17) years. After matching, intraoperative TEE was significantly associated with a lower 30-day mortality rate compared with no TEE: 3.81% vs 5.27% (odds ratio [OR], 0.69 [95% CI, 0.67-0.72]; P  < .001), a lower incidence of stroke or 30-day mortality: 5.56% vs 7.01% (OR, 0.77 [95% CI, 0.74-0.79]; P  < .001), and a lower incidence of reoperation or 30-day mortality: 7.18% vs 8.87% (OR, 0.78 [95% CI, 0.76-0.80]; P  < .001). Results were similar across all matched comparisons (including within-hospital, within-surgeon matched analyses) and were robust to a negative control and sensitivity analyses.

Conclusions and Relevance   Among adults undergoing cardiac valve or proximal aortic surgery, intraoperative TEE use was associated with improved clinical outcomes in this cohort study. These findings support routine use of TEE in these procedures.

Each year, 150 000 patients undergo high-risk, 1 - 3 open cardiac valve or proximal aortic surgery in the US. 4 Transesophageal echocardiography (TEE) is an ultrasonography-based, cardiac imaging tool used in cardiac surgical procedures to facilitate informed surgical decision making 5 - 7 and manage intraoperative complications. 5 - 9 However, the current American Heart Association (AHA) and American College of Cardiology (ACC) guidelines do not specifically recommend for or against the use of intraoperative TEE in the majority of cardiac surgical procedures 10 - 12 because prior to 2020, observational studies have not directly associated intraoperative TEE with improved clinical outcomes. 5 - 9 Recently, evidence has begun to accumulate on improved outcomes with TEE use after cardiac valve and coronary artery bypass graft (CABG) surgical procedures. 13 - 15 But there is no research directly comparing outcomes after proximal aortic surgery with TEE vs without TEE, and the existing study on improved outcomes with TEE after cardiac valve repair or replacement surgery used administrative claims data. 15

To go beyond prior observational work using claims data, 15 , 16 this study aimed to test the association between intraoperative TEE and clinical outcomes using data from the Society of Thoracic Surgeon (STS), Adult Cardiac Surgery Data (ACSD) registry database. These STS data allowed the application of rigorous statistical matching techniques to directly compare similar patients who underwent cardiac valve or proximal aortic surgery with vs without intraoperative TEE. We hypothesized that intraoperative TEE would be associated with a decreased incidence of 30-day mortality, stroke or 30-day mortality, and reoperation or 30-day mortality.

The STS ACSD contains 6.9 million surgical records, has 3800 participating surgeons, and encompasses more than 90% of the hospitals that perform cardiac surgery in the US. 17 For this analysis, data across STS ACSD versions 2.73, 2.81, and 2.90 were queried. 18 All data management and statistical analyses were performed in accordance with the STS Participant User Files Data Use Agreement. Our study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline for observational studies. 19 All aspects of this study were reviewed and approved by the University of Pennsylvania institutional review board and informed consent was waived given the deidentified nature of the data.

This study’s data (including race and ethnicity) were collected as variables retrospectively by each institution participating in the STS ACSD. Because this study analyzed national data across multiple institutions, it is unknown how each institution recorded race and ethnicity (eg, whether by electronic medical record or by patient report). However, the STS Research Center quality-controls all variables by audit regularly.

The study cohort consisted of all patients aged at least 18 years undergoing at least one of the following surgical procedures between July 1, 2011, and June 30, 2019: (1) open valve (aortic, mitral, pulmonic, or tricuspid) repair or replacement; (2) open, ascending aortic, and/or proximal aortic arch surgery (eg, aortic root replacement with a valved conduit, aortic valve sparing root, aortic homograft, or nonvalved conduit replacement with or without aortic hemiarch replacement). Patients were excluded if undergoing any of the following surgical procedures: (1) isolated CABG surgery; (2) isolated other cardiac surgery; (3) unspecified valve repair or replacement surgery; or (4) unspecified aortic surgery.

Our primary outcome was death within 30 days of surgery. 20 Our secondary outcomes were (1) composite of in-hospital stroke or 30-day mortality; or (2) composite of in-hospital reoperation (for bleeding, valve or CABG reintervention) or 30-day mortality. Information on outcome variable labels across STS ACSD versions (eg, 2.73, 2.81, and 2.90) may be found in eAppendix 2 in the Supplement .

The exposure variable was receipt of an intraoperative TEE. This was defined using the STS ACSD variable called intraoperative TEE post procedure (consistent across versions 2.73, 2.81, and 2.90).

Independent covariates were used for matching. The categories included: demographics, admission status, preexisting comorbidities, hemodynamic data, laboratory values, intraoperative surgical variables, surgery type, surgical volume by hospital and surgeon and STS projected risk scores.

Because of baseline covariate differences (eAppendix 1 in the Supplement ) between patients undergoing cardiac valve or proximal aortic surgery with vs without intraoperative TEE, we performed 2 matched comparisons. 21 , 22 First, an all-patient, across-hospital, across-surgeon matched comparison and a second, within-hospital, within-surgeon matched comparison. All matched analyses involved exact matching on key covariates, finely balancing the joint distribution of key nominal variables, 23 and balanced on all remaining variables. The all-patient matched comparison was based on optimal matching within propensity score caliper. The 2 within-surgeon matched comparisons were based on optimal subset matching. A detailed discussion on statistical matching methodology is presented in eAppendix 4 in the Supplement .

In the first all-patient, across-hospital, across-surgeon, matched comparison, each patient who did not receive a TEE was matched to a comparable patient who did receive TEE during surgery. To ensure each matched pair of patients were as similar as possible, we applied strict matching criteria. First, we matched exactly on New York Heart Association (NYHA) Classification (1 to 4 or absent) and projected 30-day mortality by quartile. Next, because TEE differed across surgery types (eAppendix 1 in the Supplement ), we finely balanced 23 on the 9 major surgery types, secondary procedures, an indicator of previous cardiac surgery, (ie, redo sternotomy) normal ejection fraction (eg, EF greater than or equal to 55%), preexisting hypertension, and admission status. Finally, we balanced all other variables under the categories of demographics, preexisting conditions, hemodynamic data, laboratory values, intraoperative surgical variables, cardiac surgical volume (by hospital and surgeon), and surgery type. Optimal matching within propensity score calipers was implemented using the R package bigmatch. See eAppendix 4 in the Supplement for details on the overall statistical matching methodology and for the all-patient matched comparison.

Because there were differences in TEE by hospital and by surgeon (eAppendix 3 in the Supplement ), we elected to undertake a second, within-hospital, within-surgeon, matched comparison. For this analysis, each patient undergoing a valve or aortic surgery at a given hospital, by a given surgeon with TEE, was matched to a similar patient undergoing valve or aortic surgery at that same hospital by the same surgeon without TEE. Because intraoperative TEE varied predominantly by surgery type (eAppendix 1 in the Supplement ), we applied exact matching to all 9 surgical categories. Covariates used for exact matching included (1) hospital; (2) surgeon; (3) all 9 surgery types; (4) normal EF ( at least 55%); (5) NYHA Classification; and (6) projected 30-day mortality (by quartile). As was done in the all-patient match, we balanced all other variables as specified above. To further reduce selection bias that could occur with surgeons who always (or never) used TEE during cardiac surgery, we considered only surgeons whose preference for TEE was equivocal (TEE probability range of 0.30 to 0.70) ( Figure 1 ). Finally, we elected to conduct an additional, supplementary, within-hospital, within-surgeon matched comparison across all surgeons, regardless of intraoperative TEE frequency (TEE probability range 0.00 to 1.00). To characterize differences in outcomes by surgery type (with TEE vs without), we performed subgroup analyses among patients undergoing similar surgical procedures. These subgroups were categorized based on anatomical location of surgery, surgery type, and those with similar risk profiles. The surgical procedures classified into each subgroup may be found in eAppendix 7 in the Supplement . Statistical matching was implemented using the R package rcbsubset with default settings.

The quality of statistical matching was assessed using standardized differences (SD). A match was considered acceptable if all covariates had a SD less than 0.10 between the TEE and the no-TEE groups. 21 , 24 , 25

We first conducted an unmatched, unadjusted, analysis of outcomes, where patients undergoing cardiac valve or aortic surgery with vs without TEE were compared. We analyzed the binary clinical outcomes using the Fisher exact test. We next conducted an analysis of outcomes among the matched cohorts. The binary clinical outcomes were analyzed using the McNemar test. 26 , 27

Statistical sensitivity analyses were conducted to assess the robustness of our findings to unmeasured confounding using Rosenbaum bounds and amplification techniques. 27 , 28 The sensitivity analysis excluding those missing the TEE exposure was conducted using the same statistical tests as previously described. The negative control outcome analysis of elevation in postoperative creatinine was analyzed using a t test (unadjusted) and a difference-in-means estimator for the matched pair study design. 29 All hypothesis testing was 2-sided and significance was set at P  < .05. Data management, including data cleaning, data categorizing, and merging across ACSD versions was done using Stata version 15.0 (StataCorp). Additional data management required for matching and statistical analyses were conducted by R version 4.0.3 (R Project for Statistical Computing) using the R package dplyr. 30 , 31 Statistical analysis was performed from October 2020 to April 2021. A link to the GitHub code repository is provided in eAppendix 11 in the Supplement .

Following exclusions ( Figure 2 ), our study cohort included 872 936 patients undergoing valve or aortic surgery. Of the 872 936 patients, 540 229 (61.89%) were male, 63 565 (7.28%) were Black, 742 384 (85.04%) were White, 711 326 (81.5%) received TEE, and 161 610 (18.5%) did not receive TEE; the mean (SD) age was 65.61 years (13.17) years. Compared with patients who did not receive TEE, those who did receive TEE were similar demographically and hemodynamically, but had higher rates of preexisting comorbidities, ( Table 1 ) and varied by surgery type. The complete baseline characteristics between the TEE vs no TEE groups and the TEE distribution by surgery type are presented in eAppendix 1 in the Supplement .

Overall, 39 078 patients (4.32%) died within 30 days. Patients who received an intraoperative TEE had a lower 30-day mortality: 3.92% vs 5.27% (odds ratio [OR], 0.73 [95% CI, 0.72-0.75]; P  < .001), a lower incidence of stroke or 30-day mortality: 5.63% vs 7.01% (OR, 0.79 [95% CI, 0.77-0.81]; P  < .001), and a lower incidence of reoperation or 30-day mortality: 7.31% vs 8.87% (OR, 0.81 [95% CI, 0.79-0.83]; P  < .001). Unadjusted outcomes reported in McNemar format may be found in eAppendix 6 in the Supplement .

Our first, across-hospital, across-surgeon match consisted of 161 610 matched pairs that were similar in observable covariates ( Table 2 ). After matching, standardized differences across all variables were less than 0.10. The full covariate balance after matching is presented in eAppendix 5 in the Supplement . Our second, within-hospital, within equivocal-TEE-surgeon match consisted of 22 739 matched pairs that were similar in observable covariates; all with standardized differences less than 0.10. The full covariate balance is presented in eAppendix 5 in the Supplement .

The all patient across-hospital, across-surgeon matched analysis found that that among 161 610 matched pairs, intraoperative TEE was significantly associated with a lower 30-day mortality rate: 3.81% vs 5.27% (OR, 0.69 [95% CI, 0.67-0.72]; P  < .001), a lower incidence of stroke or 30-day mortality: 5.56% vs 7.01% (OR, 0.77 [95% CI, 0.74-0.79]; P  < .001), and a lower incidence of reoperation or 30-day mortality: 7.18% vs 8.87% (OR, 0.78 [95% CI, 0.76-0.80]; P  < .001) ( Table 3 ). Outcomes reported in McNemar format may be found in eAppendix 6 in the Supplement .

The within-hospital, within-surgeon with equivocal TEE preference (TEE probability: 0.30-0.70), matched analysis found that among 22 739 matched pairs, intraoperative TEE was significantly associated with a lower 30-day mortality rate: 2.79% vs 3.22% (OR, 0.86 [95% CI, 0.77-0.96]; P  = .008) and a lower incidence of stroke or 30-day mortality: 4.38% vs 4.76% (OR, 0.91 [95% CI, 0.83-1.00]; P  = .048). Intraoperative TEE was not statistically significantly associated with a lower incidence of reoperation or 30-day mortality: 5.58% vs 5.77% (OR, 0.94 [95% CI, 0.85-1.04]; P  = .24) ( Table 3 ). The 30-day mortality on the within-hospital, within-surgeon matched comparison was approximately 1% to 2% lower than the all-patient, across-hospital, across-surgeon matched comparison (30-day mortality with TEE: 3.81% on all-patient, across-hospital, across-surgeon matched comparison vs 2.79% on the within-hospital, within-surgeon, matched comparison; 30-day mortality without TEE: 5.27% on all-patient, across-hospital, across-surgeon matched comparison vs 3.22% on the within-hospital, within-surgeon, matched comparison) ( Table 3 ). Outcomes reported in McNemar format may be found in eAppendix 6 in the Supplement . An additional supplementary within-hospital, within-surgeon matched comparison across all surgeons, regardless of the probability of intraoperative TEE use (65 340 matched pairs), found comparable results (eAppendix 6 in the Supplement ). Additional subgroup analyses investigating outcomes with TEE vs without among patients indicated that patients undergoing mitral valve replacement or proximal aortic surgical procedures seem to benefit more from TEE compared with the overall cohort. These results are presented in eAppendix 7 in the Supplement .

To test the robustness of our findings, we completed sensitivity analyses and a negative control outcome analysis. The first sensitivity analysis indicated that according to the Rosenbaum bounds and associated amplification analysis, 27 , 28 to nullify the primary outcome finding from the all-patient, across-hospital, across-surgeon matched comparison, it would take an unmeasured confounder that doubled the odds of 30-day mortality and tripled the odds of TEE use (eAppendix 8 in the Supplement ). To nullify the primary outcome finding from the within-hospital, within-surgeon matched comparison, it would take an unmeasured confounder that increased the odds of 30-day mortality by 40% and the odds of TEE use by more than 40% (eAppendix 8 in the Supplement ). The second sensitivity analysis tested the robustness of our results by excluding the 2% of the cohort missing the TEE exposure and revealed findings that agreed with our presented results (eAppendix 9 in the Supplement ). Finally, our negative control outcome analysis compared elevation in postoperative creatinine between the TEE and no-TEE groups. Three of the 4 negative control outcome analyses were either statistically insignificant or incongruent with the primary results—an additional indication that residual confounding was controlled (eAppendix 6 in the Supplement ). A detailed explanation of the negative control outcome including rationale for selection and interpretation of the results is presented in eAppendix in the Supplement .

Among 872 936 patients undergoing valve or aortic surgery, across all analyses, intraoperative TEE was statistically significantly associated with a lower 30-day mortality, a lower incidence of stroke or 30-day mortality, and in the all-patient match, TEE was statistically significantly associated with a lower incidence of reoperation or 30-day mortality. These results were supported by multiple sensitivity analyses 27 , 28 that established the presented results would remain statistically significant at a .05 level in the presence of an unmeasured confounder that doubled the odds of 30-day mortality and tripled the odds of intraoperative TEE use, suggesting the presented findings would be robust to residual, unmeasured confounding. 22 , 28

Current AHA/ACC guidelines 10 , 11 do not specifically recommend for or against the use of intraoperative TEE for all cardiac valve replacement surgical procedures, 10 most cardiac valve repair surgical procedures, 10 and all proximal aortic aneurysm surgical procedures. 11 Presumably, this equivocal, class IIa, AHA/ACC stance on intraoperative TEE use is due to the absence of research comparing clinical outcomes among patients undergoing cardiac surgery with vs without TEE. 5 , 6 , 8 , 9 Only very recently has the impact of intraoperative TEE on clinical outcomes among patients undergoing cardiac surgery with vs without TEE been directly compared. 14 - 16

The current study’s finding that intraoperative TEE is associated with improved clinical outcomes is consistent with recent previous comparative effectiveness research by both our group 15 , 16 and others. 14 In 2020, we used propensity score matching to compare 219 238 Medicare beneficiaries undergoing cardiac valve surgery and found TEE was associated with a lower 30-day mortality. 15 In 2021, we used instrumental variable methods to compare 114 871 Medicare beneficiaries undergoing isolated CABG surgery and found that TEE was associated with lower in-hospital stroke and lower 30-day mortality. 13 Subsequently, an independent study by Metkus and colleagues used STS data and propensity score matching to compare 1.3 million patients undergoing isolated CABG surgery with vs without TEE and found a mortality benefit to the use of TEE. 14

The current study improves upon our previous work 15 , 16 in several noteworthy respects. First, the detailed, patient-level data found in the STS ADCS data registry allowed us to apply very strict matching criteria in order to minimize patient-level differences, controlling for far more observed patient-level covariate differences between those undergoing cardiac surgery with TEE vs without TEE. Second, the size of this STS cohort afforded us the opportunity to undertake within-hospital, within-surgeon matches. By creating matched pairs of 2 patients (1 with TEE vs 1 without TEE) admitted to the same hospital, and operated on by the same surgeon, we reduced hospital-level, and surgeon-level, unobserved confounding that could have biased our results. Third, in this study the exposure variable of TEE was found to be a true, intraoperative TEE; an improvement on our previous that could only identify a TEE within a hospitalization. 15 , 16 , 32 Fourth, by performing comprehensive sensitivity analyses, we were able to quantify how much residual, unobserved confounding would be required to alter the conclusions of our analyses. Across all analyses, our findings indicate an association between TEE and improved perioperative outcomes after open cardiac valve or proximal aortic surgery.

Although this matched retrospective observational study cannot elucidate the exact reasons for the clinical outcomes benefit observed with intraoperative TEE, it is likely that intraoperative TEE is conferring some degree of benefit because the association persisted on the strict, within-hospital, within-surgeon matched comparisons. Diagnostic information provided by TEE, interpreted by an experienced echocardiographer—cardiologist or anesthesiologist—could identify surgical complications 5 - 9 and improve outcomes by facilitating informed intraoperative decision making by the cardiac surgeon. 5 - 7 For instance, in valve surgery, paravalvular regurgitation identified by TEE after valve repair or replacement could prompt an immediate valve revision 5 , 6 and reduce the risk of reoperation (along with the complications associated with a second surgery). Additionally, TEE imaging can reduce the risk of stroke from air embolism by ensuring the dissipation intracardiac air prior to separation from cardiopulmonary bypass 33 or decrease the incidence of embolic stroke by ensuring an aortic cannulation or aortic cross clamp site does not embolize atheromatous plaque. 33 - 36 But equal to diagnostic information provided by the TEE imaging itself, it is possible that the association between intraoperative TEE and improved outcomes in this study could be related to the availability of an experienced cardiologist or anesthesiologist certified to perform and interpret a TEE in the operating room.

Our study must be interpreted with awareness of its limitations. First, the observational, nonrandomized design of this study cannot confirm a causal link between TEE and improved clinical outcomes because of the inability to completely eliminate residual confounding; particularly related to inherent differences among those who did not receive TEE. For instance, residual unobserved confounding could be introduced by anatomical considerations at the patient-level or differences in intraoperative management and TEE performance at the clinician-level that might indicate systematic differences among those who did not receive TEE compared with those who did receive TEE. An example of patient-level confounding could be introduced by our inability to exclude patients with anatomical contraindications to TEE such as esophageal (eg, esophagectomy, varices, or strictures) 37 or gastric (eg, previous gastric bypass surgery, gastric ulcer, or hiatal hernia) 37 diagnoses. But given the consistent results across all analyses, and the rare prevalence of these diagnoses (<0.5%), 37 we are reassured that residual patient-level confounding would not change the stated results. An example of clinician-level confounding could be related to the availability of a clinician with the specialization to perform an intraoperative TEE (eg, cardiologist or anesthesiologist). This example of clinician-level confounding could have persisted even after the within-hospital, within-surgeon match because STS data does not identify the clinician performing the intraoperative TEE. Second, while the within-hospital, within-surgeon matched completely controlled for TEE preference by surgical type because we exactly matched on all 9 surgical procedures, there is the possibility that we could not fully adjust for a surgeon who might have variability in TEE preference within the same surgery. For instance, a surgeon who would request TEE for a complex mitral repair, but not request TEE for a more straightforward mitral repair. Third, the 30-day mortality on the within-hospital, within-surgeon matched comparison was 1% to 2% lower than the all-patient, across-hospital, across-surgeon matched comparison. This difference in mortality could be an indication that comparing only surgeons with a probability for intraoperative TEE between 0.30 and 0.70 may represent a different, less sick, patient population compared with the all-patient, across-hospital, across-surgeon match. Fourth, fewer than 23% of patients receiving TEE are included in the matched analyses which potentially limits the generalizability of the stated results. Nevertheless, because results were similar across all analyses—including comprehensive sensitivity analyses—we are reassured of the robustness of the stated results indicating a clinical outcomes benefit to the use of TEE in cardiac valve or aortic surgery.

The current study, particularly in combination with recent observational research demonstrating a consistent outcomes benefit to the use of TEE during cardiac valve 15 and CABG surgery 14 , 16 may have important health policy implications. Because lack of equipoise, it is unlikely that a randomized controlled trial comparing TEE vs no TEE among cardiac surgical patients undergoing cardiac valve or aortic surgery would ever be conducted. Thus, rigorous observational studies such as the current work and previous work 15 , 16 are required to inform future AHA/ACC guideline recommendations for the routine use of TEE in cardiac surgery.

This cohort study found that the use of intraoperative TEE was associated with a lower 30-day mortality and a lower incidence of stroke or 30-day mortality among patients undergoing open cardiac valve or aortic surgery. These findings provide evidence to support the routine use of intraoperative TEE in all open cardiac valve and proximal aortic surgical procedures.

Accepted for Publication: December 16, 2021.

Published: February 9, 2022. doi:10.1001/jamanetworkopen.2021.47820

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 MacKay EJ et al. JAMA Network Open .

Corresponding Author: Emily J. MacKay, DO, MSHP, University of Pennsylvania, 423 Guardian Dr, 310 Blockley Hall, Philadelphia, PA 19104 ( [email protected] ; [email protected] ).

Author Contributions: Dr MacKay had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: MacKay, Zhang, Desai.

Drafting of the manuscript: MacKay, Augoustides, Desai.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: MacKay, Zhang, Groeneveld, Desai.

Obtained funding: MacKay.

Administrative, technical, or material support: Groeneveld.

Supervision: Augoustides, Desai.

Conflict of Interest Disclosures: None reported.

Funding/Support: This work was funded by (1) the Foundation for Anesthesia Education and Research (FAER) Mentored Research Training Grant (MRTG) (MRTG-08-15-2020; 581700) to Dr MacKay; (2) Department of Anesthesiology and Critical Care, University of Pennsylvania to Dr MacKay. The Department of Anesthesiology and Critical Care at the University of Pennsylvania funding (to Dr MacKay) provided the resources to purchase the Adult Cardiac Surgery Data (ACSD) from the Society of Thoracic Surgeons (STS) national registry.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, and approval of the manuscript; and decision to submit the manuscript for publication.

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Collection  12 March 2023

Top 100 in Chemistry - 2022

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research paper of 2022

Navigating the labor mismatch in US logistics and supply chains

As the US economy recovers postpandemic, demand for labor has outstripped supply. Companies are facing the “ Great Attrition ,” coupled with increased competition for labor. The transportation and logistics sector has been particularly hard hit, with the impact of worker-retention challenges and rising labor costs being felt across the entire value chain.

The labor mismatch has pushed private-sector wages to increase at more than double the long-term pre-COVID-19 growth rates, yet positions remain unfilled. There are several underlying factors for this imbalance. Some are directly related to the impact of the COVID-19 pandemic and are therefore likely to be temporary. There are indications, however, that deeper structural shifts are at play that could have a longer-lasting impact on labor supply and demand. On the supply side, evolving work preferences and accelerated retirement may continue for some time; likewise, demand shifts from services to goods also appear to have some staying power.

Addressing the challenges is not easy, and focusing on recruitment and pay may not be sufficient to resolve the issue. Successfully navigating the current labor mismatch requires a comprehensive set of coordinated actions that address labor issues and their effects across the value chain. Nevertheless, there are actions executives can take to respond.

The 2021 labor mismatch has had a profound impact on US businesses

The United States’ post-COVID-19 economic recovery has seen an unusual reduction in labor-force participation. Jobs are available—the job-openings rate is around 50 percent above prepandemic levels—but the workforce to fill them has contracted. About four million people have left the civilian workforce (Exhibit 1).

With demand for workers exceeding supply, the cost of labor has increased accordingly. Private-sector nominal-wage growth is more than double the long-term pre-COVID-19 pace—more than triple when adjusted for the consumer price index (CPI). Transport and warehousing labor has been most affected in terms of cost, with wages increasing four times faster than before the pandemic.

Despite wage increases, logistics operations are still having difficulty hiring and retaining frontline workers, while also seeing increased absenteeism, causing knock-on effects across the supply chain. Suppliers’ on-time delivery rates are falling, a situation exacerbated by supply shortages. “On orders” are being cut at greater rates and experiencing significant delays, driving even further volatility in order patterns. Companies that employ third-party logistics services are also experiencing considerable challenges, such as transport rates increasing by up to 30 percent.

The labor mismatch is unlikely to dissipate on its own

What’s striking about the current labor challenge is that, unlike in the past, higher wages alone have not led to positions being filled. There are several underlying factors for this imbalance—some may be temporary, while others are long lasting. There are also regional differences, and in some cases labor availability varies significantly at different zip-code and skill-level combinations.

Some factors related to the COVID-19 pandemic are beginning to dissipate. For example, the federally enhanced unemployment-benefits program wound down in September. Workers who left their jobs because of health concerns or to take care of family members or children at home due to school or childcare-facility closures may return to work. 1 A McKinsey survey found that among respondents who had left their jobs, 45 percent cited the need to take care of family as an influential factor in their decision. See “ ‘Great Attrition’ or ‘Great Attraction’? The choice is yours ,” McKinsey Quarterly , September 8, 2021. And training programs that were suspended due to the pandemic, such as those provided by driving schools, have largely resumed.

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Other factors, however, could lead to more permanent shifts in the labor supply. The relationship between job openings and unemployment has departed from past trends and appears to be driven by fundamental shifts in labor supply-and-demand curves (Exhibit 2). Further evidence that the drop in labor-force participation is underpinned by systemic causes is the fact that the decline in labor supply can be seen across all worker types and demographics, including gender, age, marital status, and whether the person works part time or full time.

Furthermore, since the start of the pandemic, more than 15.9 million people have relocated within the United States. In the same time period, there has been a noticeable increase in the number of people taking early retirement, as 1.7 million workers retired from the labor force earlier than expected. 2 Owen Davis et al., “The pandemic retirement surge increased retirement inequality,” The New School Schwartz Center for Economic Policy Analysis, June 1, 2021, economicpolicyresearch.org. Immigration rates also have a lasting impact on labor supply, and the net immigration rate in the United States fell by 1.3 percent between 2020 and 2021. 3 “U.S. net migration rate 1950–2021,” United Nations World Population Prospects, accessed on November 2, 2021, macrotrends.net.

Last, a change in mindset toward work may also be an underlying factor of long-term shifts in labor supply. McKinsey research indicates a disconnect  between why employers think their staff are leaving and why employees are actually leaving their jobs. Employers are looking at transactional factors, such as compensation or alternative job offers, but these are not the primary drivers of attraction or attrition. Employees place greater value on relational elements, such as a sense of belonging or having caring and trusting teammates at work.

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How COVID-19 is reshaping supply chains

There is also uncertainty over how supply-chain labor demand will continue to evolve. The growth in e-commerce, for example, has driven new demand for supply-chain labor that is likely to remain postpandemic. Recently signed infrastructure legislation is projected to further increase labor demand: industries within the construction value chain are likely to require an additional one million workers if the projected 30 percent of Infrastructure Investment and Jobs Act (IIJA) funds are spent by 2025. 4 Infrastructure Investment and Jobs Act draft, August 2021; EMSI; US Bureau of Labor Statistics. Since the logistics and construction industries typically attract similar pools of labor supply, the impact of such legislation would extend multiple years into the future. Additionally, the shift in consumer spending from services toward goods during the COVID-19 pandemic, which added supply-chain pressure to refill fast-selling products, may also stick.

Several industries are also experiencing drastic changes in demand. The travel and food-services industries, for example, saw severe demand drops and responded by furloughing or laying off workers and accelerating early retirements. These measures may have contributed to structural shifts in the labor market for these industries. The trucking industry was facing falling numbers of drivers before the pandemic because of multiple factors, including generational demographics, age limits, time away from home, and drug tests. The pandemic compounded the problem: on one hand, more people ordered goods to their homes, which changed how the deliveries were made and further increased the demand for truck drivers—and on the other, the closure of truck-driving schools, combined with a pull of labor supply away from driving toward construction, reduced the supply of labor.

A meaningful intervention for the mismatch

Together, these factors mean that the labor mismatch in US supply chains is unlikely to dissipate quickly, with imbalances in supply and demand persisting. So what can companies do to address this imbalance now? In this unprecedented environment, companies may have to look beyond the traditional levers of recruitment and retention, and also implement a comprehensive set of coordinated actions to address the labor shortage. For interventions to be meaningful, they need to address the full value chain.

This seems challenging, but there are reasons to be optimistic. Companies are seeing meaningful shifts in their labor-supply profiles by taking the following steps.

Ensuring viability of the supplier base. Companies can engage suppliers with large labor forces—for example, temporary labor, food services, janitorial services, and third-party transportation—to ensure operational viability or identify alternative suppliers that can reduce first- and second-tier supplier risk.

Reimagining the job of a driver and warehouse worker

One logistics company used advanced analytics, including machine-learning techniques and web scraping more than 50,000 reviews, to identify causes of worker attrition among its drivers and distribution-center employees. It found that the physical nature of the job, lack of work–life balance, and scheduling issues were key drivers of attrition.

The company then designed a range of interventions to mitigate these issues, including a leadership training program for supervisors and managers to address frontline grievances. It also provided greater flexibility in scheduling and pay, and collaborated with customers to solve the root causes of employee-satisfaction problems—such as SKUs that were difficult to pick and deliveries that were scheduled for inconvenient times.

Finally, the company developed an implementation structure and stood up a project-management office to ensure that initiatives were successfully implemented. In distribution centers where changes had been implemented, worker retention improved by about 10 to 15 percent; the company sought to scale those gains across the organization.

Reimagine the employee value proposition—beyond wages. Companies that solved for competitive wages and built attractive value propositions for employees have found it easier to retain their workforces. In addition to proactively adjusting wages to stay ahead of competitors (especially in highly competitive markets), or embarking on aggressive recruitment campaigns, companies can deploy analytics to pinpoint drivers of attrition—and make bold changes where it matters most (see sidebar “Reimagining the job of a driver and warehouse worker”).

Create capability to identify the stressed nodes and adjust labor flows. Companies can take measures to shift network flow away from labor-stressed nodes, especially where labor supply varies across regions. For example, orders could be rerouted to other warehouses, or products could be manufactured in locations that are less stressed from a labor-supply standpoint. Reformulating or redesigning products can help as well by reducing the need for labor-constrained components and ingredients.

Increasing output by reducing complexity

A consumer-goods company was able to increase productivity by cutting 30 percent of its product portfolio with limited impact on sales. It achieved this by defining the labor cost and complexity of each product, deploying advanced analytics to estimate the substitutability of each product, and conducting an assortment and optimization simulation to identify which SKUs to delist (exhibit).

Reduce complexity and labor content of products and services. Companies can reassess their product and service portfolios by building a robust understanding of each offering’s operational and commercial trade-offs. One company was able to increase throughput at its factories and warehouses by optimizing its product portfolio (see sidebar “Increasing output by reducing complexity”).

Explore lean management and automation. Companies may reduce reliance on labor across the supply chain over the long term through product reengineering, lean-management transformation, and automation. Furthermore, automation could help companies improve employee engagement and satisfaction. More than 40 percent of employees spend at least a quarter of their time performing manual and repetitive tasks. In some cases, automation can help not just reduce labor demand, but also allow employees to spend more of their time on higher-value, meaningful work.

Engage customers and suppliers on cost and service. Companies can engage customers on value-based offerings. They can also engage suppliers through cleansheet—based negotiations that build in complete cost-to-serve estimates, such as cost differences for labor-intensive activities, and service factors such as lead times and delivery windows.

Unlock new sources of labor supply. Companies can explore new sources of labor supply—for example prison-, juvenile-, or veteran-transition programs—or adapt roles for non-English speakers and reskill workers from declining industries or roles.

Bolster HR processes. They can also streamline and strengthen interview and onboarding processes—for example, by setting up “talent war rooms” to focus on such interventions.

Leveraging people analytics to improve frontline retention

A trucking company successfully deployed people analytics to improve frontline retention. First, it identified the top quartile of drivers who were most likely to leave the company. Analysis of this high-risk population allowed the company to identify the key drivers of employee dissatisfaction and implement targeted interventions. These interventions led to an improvement of more than 20 percent in new-driver retention, a 15 percent increase in the number of driver applications, and a more than 30 percent increase in the number of new hires, which translated to a 10 percent-plus increase in revenue potential (exhibit).

Deploy advanced people analytics. Companies can leverage people analytics, such as cluster analytics and attribution models, on internal and external data to identify and prioritize interventions on segmented groups of the labor force (see sidebar “Leveraging people analytics to improve frontline retention”).

Develop agile management across functions. Companies can deploy digital performance-management tools, such as control towers, to manage labor flows. Daily cross-functional war rooms can increase visibility around labor availability and help the organization to plan and adjust accordingly.

The labor mismatch is a complex challenge, one that may be here to stay for a while—and it is clear there is no silver-bullet solution.

The labor mismatch is a complex challenge, one that may be here to stay for a while—and it is clear there is no silver-bullet solution. Companies looking to embark on a labor-resilience transformation can take the following three steps. First, employers need to understand how labor shortages impact their suppliers, internal labor, and customers—starting with the size and impact of labor risk across operations; the severity of the labor gap by location, roles, and suppliers; and a forecast of labor dynamics in each relevant market. Second, companies could design bold interventions that structurally change both the demand and supply of the organization’s labor. Third, companies may require strong executive-level support to ensure that cross-functional initiatives are implemented effectively.

Dilip Bhattacharjee and Andrew Curley are partners in McKinsey’s Chicago office; Felipe Bustamante is an associate partner in the Miami office, where Fernando Perez is a partner.

The authors wish to thank Aditi Brodie, Mike Doheny, Travis Fagan, Ezra Greenberg, Darren Rivas, and Daniel Swan for their contributions to this article.

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