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Driven by the need to capture users' evolving interests and optimize their long-term experiences, more and more recommender systems have started to model recommendation as a Markov decision process and employ reinforcement learning to address the problem.
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Controlling Popularity Bias in Learning-to-Rank Recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys ’17) . Association for Computing Machinery, New York, NY, USA, 42–46
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FATREC Workshop on Responsible Recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys ’17) . Association for Computing Machinery, New York, NY, USA, 382–383
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Neural Factorization Machines for Sparse Predictive Analytics. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’17) . Association for Computing Machinery, New York, NY, USA, 355–364
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Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW ’17) . International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 173–182
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IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’17) . Association for Computing Machinery, New York, NY, USA, 515–524
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Fairness-Aware Group Recommendation with Pareto-Efficiency. In Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys ’17) . Association for Computing Machinery, New York, NY, USA, 107–115
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Runaway Feedback Loops in Predictive Policing. In Conference on Fairness, Accountability and Transparency . PMLR, 160–171
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Rainbow: Combining Improvements in Deep Reinforcement Learning. In Thirty-Second AAAI Conference on Artificial Intelligence
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2nd FATREC Workshop: Responsible Recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18) . Association for Computing Machinery, New York, NY, USA, 516
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Fairness-Aware Explainable Recommendation over Knowledge Graphs
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Counteracting Bias and Increasing Fairness in Search and Recommender Systems. In Fourteenth ACM Conference on Recommender Systems (RecSys ’20) . Association for Computing Machinery, New York, NY, USA, 745–747
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Bandit Algorithms
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Off-Policy Learning in Two-Stage Recommender Systems. In Proceedings of The Web Conference 2020 (WWW ’20) . Association for Computing Machinery, New York, NY, USA, 463–473
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Ji Yang, Minmin Chen, Jiaxi Tang, Lichan Hong, and Ed H. Chi. 2020 · 2020
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Towards a Fair Marketplace: Counterfactual Evaluation of the Trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM ’18) . Association for Computing Machinery, New York, NY, USA, 2243–2251
Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, and Fernando Diaz. 2018 · 2018
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Fairness of Exposure in Rankings. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’18) . Association for Computing Machinery, New York, NY, USA, 2219–2228
Ashudeep Singh and Thorsten Joachims. 2018 · 2018
Cited alongside, same era.
Multistakeholder Recommendation with Provider Constraints. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18) . Association for Computing Machinery, New York, NY, USA, 54–62
Özge Sürer, Robin Burke, and Edward C. Malthouse. 2018 · 2018
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Reinforcement Learning: An Introduction (second ed.)
Richard S. Sutton and Andrew G. Barto. 2018 · 2018
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Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’18) . Association for Computing Machinery, New York, NY, USA, 1040–1048
Xiangyu Zhao, Liang Zhang, Zhuoye Ding, Long Xia, Jiliang Tang, and Dawei Yin. 2018 · 2018
Cited alongside, same era.
DRN: A Deep Reinforcement Learning Framework for News Recommendation. In Proceedings of the 2018 World Wide Web Conference (WWW ’18) . International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 167–176
Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, and Zhenhui Li. 2018 · 2018
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Fairness-Aware Tensor-Based Recommendation. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM ’18) . Association for Computing Machinery, New York, NY, USA, 1153–1162
Ziwei Zhu, Xia Hu, and James Caverlee. 2018 · 2018
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Transparent, Scrutable and Explainable User Models for Personalized Recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19) . Association for Computing Machinery, New York, NY, USA, 265–274
Krisztian Balog, Filip Radlinski, and Shushan Arakelyan. 2019 · 2019
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Controlling Fairness and Bias in Dynamic Learning-to-Rank. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’20) . Association for Computing Machinery, New York, NY, USA, 429–438
Marco Morik, Ashudeep Singh, Jessica Hong, and Thorsten Joachims. 2020 · 2020
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Achieving Fairness in the Stochastic Multi-Armed Bandit Problem
Vishakha Patil, Ganesh Ghalme, Vineet Nair, and Y. Narahari. 2020 · 2020
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FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms. In Proceedings of The Web Conference 2020 (WWW ’20) . Association for Computing Machinery, New York, NY, USA, 1194–1204
Gourab K Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, and Abhijnan Chakraborty. 2020 · 2020
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Neural Collaborative Filtering vs. Matrix Factorization Revisited. In Fourteenth ACM Conference on Recommender Systems (RecSys ’20) . Association for Computing Machinery, New York, NY, USA, 240–248
Steffen Rendle, Walid Krichene, Li Zhang, and John Anderson. 2020 · 2020
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Dealing with Bias and Fairness in Data Science Systems: A Practical Hands-on Tutorial
Pedro Saleiro, Kit T. Rodolfa, and Rayid Ghani. 2020 · 2020
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Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards. In International Conference on Machine Learning . PMLR, 8905–8915
Umer Siddique, Paul Weng, and Matthieu Zimmer. 2020 · 2020
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Self-Supervised Reinforcement Learning for Recommender Systems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’20) . Association for Computing Machinery, New York, NY, USA, 931–940
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Combinatorial Multi-Armed Bandits with Concave Rewards and Fairness Constraints. In Twenty-Ninth International Joint Conference on Artificial Intelligence , Vol. 3. 2554–2560
Huanle Xu, Yang Liu, Wing Cheong Lau, and Rui Li. 2020 · 2020
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Xiangyu Zhao, Xudong Zheng, Xiwang Yang, Xiaobing Liu, and Jiliang Tang. 2020 · 2020
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Pseudo Dyna-Q: A Reinforcement Learning Framework for Interactive Recommendation. In Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM ’20) . Association for Computing Machinery, New York, NY, USA, 816–824
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Estimating and Penalizing Preference Shift in Recommender Systems. In Fifteenth ACM Conference on Recommender Systems (RecSys ’21) . Association for Computing Machinery, New York, NY, USA, 661–667
Micah Carroll, Dylan Hadfield-Menell, Stuart Russell, and Anca Dragan. 2021 · 2021
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The Zoo of Fairness Metrics in Machine Learning
Alessandro Castelnovo, Riccardo Crupi, Greta Greco, and Daniele Regoli. 2021 · 2021
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Reinforcement Recommendation with User Multi-Aspect Preference. In Proceedings of the Web Conference 2021 (WWW ’21) . Association for Computing Machinery, New York, NY, USA, 425–435
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SimuRec: Workshop on Synthetic Data and Simulation Methods for Recommender Systems Research. In Fifteenth ACM Conference on Recommender Systems (RecSys ’21) . Association for Computing Machinery, New York, NY, USA, 803–805
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Charles Evans and Atoosa Kasirzadeh. 2021 · 2021
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The (Im)Possibility of Fairness: Different Value Systems Require Different Mechanisms for Fair Decision Making
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Towards Long-Term Fairness in Recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM ’21) . Association for Computing Machinery, New York, NY, USA, 445–453
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Reinforcement Learning for Information Retrieval. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’21) . Association for Computing Machinery, New York, NY, USA, 2669–2672
Alexander Kuhnle, Miguel Aroca-Ouellette, Anindya Basu, Murat Sensoy, John Reid, and Dell Zhang. 2021 · 2021
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Tutorial on Fairness of Machine Learning in Recommender Systems. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’21) . Association for Computing Machinery, New York, NY, USA, 2654–2657
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Matthieu Zimmer, Claire Glanois, Umer Siddique, and Paul Weng. 2021 · 2021
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