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Simulators can provide valuable insights for researchers and practitioners who wish to improve recommender systems, because they allow one to easily tweak the experimental setup in which recommender systems operate, and as a result lower the cost of identifying general trends and uncovering novel findings about the candidate methods.
RecSim: A Configurable Simulation Platform for Recommender Systems
Eugene Ie, Chih wei Hsu, Martin Mladenov, Vihan Jain, Sanmit Narvekar, Jing Wang, Rui Wu, and Craig Boutilier. 2019b · 1909
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Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, and Shixiang Gu. 2020 · 2006
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Being Accurate is Not Enough: How Accuracy Metrics Have Hurt Recommender Systems. In CHI ’06 Extended Abstracts on Human Factors in Computing Systems (Montréal, Québec, Canada) (CHI EA ’06) . Association for Computing Machinery, New York, NY, USA, 1097–1101
Sean M. McNee, John Riedl, and Joseph A. Konstan. 2006 · 2006
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An Experimental Comparison of Click Position-Bias Models. In Proceedings of the 2008 International Conference on Web Search and Data Mining (Palo Alto, California, USA) (WSDM ’08) . Association for Computing Machinery, New York, NY, USA, 87–94
Nick Craswell, Onno Zoeter, Michael Taylor, and Bill Ramsey. 2008 · 2008
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A Contextual-Bandit Approach to Personalized News Article Recommendation. In Proceedings of the 19th International Conference on World Wide Web (Raleigh, North Carolina, USA) (WWW ’10) . Association for Computing Machinery, New York, NY, USA, 661–670
Lihong Li, Wei Chu, John Langford, and Robert E. Schapire. 2010 · 2010
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Recommender Systems
Prem Melville and Vikas Sindhwani. 2010 · 2010
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Thompson Sampling for Contextual Bandits with Linear Payoffs. In Proceedings of the 30th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 28) , Sanjoy Dasgupta and David McAllester (Eds.). PMLR, Atlanta, Georgia, USA, 127–135
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From a “Cold” to a “Warm” Start in Recommender systems. In IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2014) . IEEE Digital Library, Parma, Italy, 1–7
Rana Chamsi Abu Quba, Salima Hassas, Hammam Chamsi, and Usama Fayyad. 2014 · 2014
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Click Models for Web Search
Aleksandr Chuklin, Ilya Markov, and Maarten de Rijke. 2015 · 2015
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Active Learning in Recommender Systems
Neil Rubens, Mehdi Elahi, Masashi Sugiyama, and Dain Kaplan. 2015 · 2015
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Interactive Recommender Systems: A Survey of the State of the Art and Future Research Challenges and Opportunities
Chen He, Denis Parra, and Katrien Verbert. 2016 · 2016
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Session-based Recommendations with Recurrent Neural Networks. In ICLR
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016 · 2016
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Recommender Systems — beyond Matrix Completion
Dietmar Jannach, Paul Resnick, Alexander Tuzhilin, and Markus Zanker. 2016 · 2016
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Continuous control with deep reinforcement learning. In ICLR
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2016 · 2016
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Unbiased Learning-to-Rank with Biased Feedback. In WSDM . 781–789
Thorsten Joachims, Adith Swaminathan, and Tobias Schnabel. 2017 · 2017
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Cold-start, warm-start and everything in between: An autoencoder based approach to recommendation. In 2017 International Joint Conference on Neural Networks (IJCNN) . 3656–3663
Angshul Majumdar and Anant Jain. 2017 · 2017
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Off-Policy Evaluation for Slate Recommendation. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS’17) . Curran Associates Inc., Red Hook, NY, USA, 3635–3645
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, and Imed Zitouni. 2017 · 2017
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Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. In ICML . 1856–1865
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. 2018 · 2018
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David Rohde, Stephen Bonner, Travis Dunlop, Flavian Vasile, and Alexandros Karatzoglou. 2018 · 2018
Cited alongside, same era.
Top-K Off-Policy Correction for a REINFORCE Recommender System. In WSDM . 456–464
Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, and Ed H. Chi. 2019 · 2019
Cited alongside, same era.
SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19 . International Joint Conferences on Artificial Intelligence Organization, 2592–2599
Eugene Ie, Vihan Jain, Jing Wang, Sanmit Narvekar, Ritesh Agarwal, Rui Wu, Heng-Tze Cheng, Tushar Chandra, and Craig Boutilier. 2019a · 2019
Cited alongside, same era.
State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study. In SIGIR . 2738–2748
Jin Huang, Harrie Oosterhuis, Bunyamin Cetinkaya, Thijs Rood, and Maarten de Rijke. 2022 · 2022
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Towards Validating Long-Term User Feedbacks in Interactive Recommendation Systems. In SIGIR . 2607–2611
Hojoon Lee, Dongyoon Hwang, Kyushik Min, and Jaegul Choo. 2022 · 2022
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Semantic Conceptions of Information
Sebastian Sequoiah-Grayson and Luciano Floridi. 2022 · 2022
Later among the works it cites.
Multi-Armed Bandits in Recommendation Systems: A Survey of the State-of-the-art and Future Directions
Nícollas Silva, Heitor Werneck, Thiago Silva, Adriano C.M. Pereira, and Leonardo Rocha. 2022 · 2022
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Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (Madrid, Spain) (SIGIR ’22) . Association for Computing Machinery, New York, NY, USA, 1347–1357
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Virtual-Taobao: Virtualizing Real-World Online Retail Environment for Reinforcement Learning
Jing-Cheng Shi, Yang Yu, Qing Da, Shi-Yong Chen, and An-Xiang Zeng. 2019 · 2019
Cited alongside, same era.
Algorithmic Effects on the Diversity of Consumption on Spotify. In Proceedings of The Web Conference 2020 (Taipei, Taiwan) (WWW ’20) . Association for Computing Machinery, New York, NY, USA, 2155–2165
Ashton Anderson, Lucas Maystre, Ian Anderson, Rishabh Mehrotra, and Mounia Lalmas. 2020 · 2020
Cited alongside, same era.
Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning Based Recommender Systems. In Proceedings of the 14th ACM Conference on Recommender Systems (Virtual Event, Brazil) (RecSys ’20) . Association for Computing Machinery, New York, NY, USA, 190–199
Jin Huang, Harrie Oosterhuis, Maarten de Rijke, and Herke van Hoof. 2020 · 2020
Cited alongside, same era.
Reinforcement Learning Algorithm for Non-stationary Environments
Sindhu Padakandla, Prabuchandran K. J., and Shalabh Bhatnagar. 2020 · 2020
Cited alongside, same era.
MARS-Gym: A Gym Framework to Model, Train, and Evaluate Recommender Systems for Marketplaces. In 2020 International Conference on Data Mining Workshops (ICDMW) . 189–197
Marlesson R. O. Santana, Luckeciano C. Melo, Fernando H. F. Camargo, Bruno Brandão, Anderson Soares, Renan M. Oliveira, and Sandor Caetano. 2020 · 2020
Cited alongside, same era.
Learning User Preferences in Non-Stationary Environments. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 130) , Arindam Banerjee and Kenji Fukumizu (Eds.). PMLR, 1432–1440
Wasim Huleihel, Soumyabrata Pal, and Ofer Shayevitz. 2021 · 2021
Cited alongside, same era.
Algorithmic Balancing of Familiarity, Similarity, & Discovery in Music Recommendations. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (Virtual Event, Queensland, Australia) (CIKM ’21) . Association for Computing Machinery, New York, NY, USA, 3996–4005
Rishabh Mehrotra. 2021 · 2021
Cited alongside, same era.
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, Canada) (SIGIR ’21) . Association for Computing Machinery, New York, NY, USA, 1023–1032
Harrie Oosterhuis. 2021 · 2021
Cited alongside, same era.
Xin Xin, Tiago Pimentel, Alexandros Karatzoglou, Pengjie Ren, Konstantina Christakopoulou, and Zhaochun Ren. 2022 · 2022
Later among the works it cites.
Evaluating Recommender Systems: Survey and Framework
Eva Zangerle and Christine Bauer. 2022 · 2022
Later among the works it cites.
Bootstrapped Personalized Popularity for Cold Start Recommender Systems. In Proceedings of the 17th ACM Conference on Recommender Systems (Singapore, Singapore) (RecSys ’23) . Association for Computing Machinery, New York, NY, USA, 715–722
Iason Chaimalas, Duncan Martin Walker, Edoardo Gruppi, Benjamin Richard Clark, and Laura Toni. 2023 · 2023
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Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (Taipei, Taiwan) (SIGIR ’23) . Association for Computing Machinery, New York, NY, USA, 238–248
Chongming Gao, Kexin Huang, Jiawei Chen, Yuan Zhang, Biao Li, Peng Jiang, Shiqi Wang, Zhong Zhang, and Xiangnan He. 2023 · 2023
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Recent Advances in the Foundations and Applications of Unbiased Learning to Rank. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (Taipei, Taiwan) (SIGIR ’23) . Association for Computing Machinery, New York, NY, USA, 3440–3443
Shashank Gupta, Philipp Hager, Jin Huang, Ali Vardasbi, and Harrie Oosterhuis. 2023 · 2023
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Olivier Jeunen. 2023 · 2023
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SCOPE-RL: A Python Library for Offline Reinforcement Learning, Off-Policy Evaluation, and Policy Selection
Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami, Ken Kobayashi, Kazuhide Nataka, and Yuta Saito. 2023 · 2023
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Norman Knyazev and Harrie Oosterhuis. 2023 · 2023
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Exploration and Regularization of the Latent Action Space in Recommendation. In WWW . 833–844
Shuchang Liu, Qingpeng Cai, Bowen Sun, Yuhao Wang, Ji Jiang, Dong Zheng, Peng Jiang, Kun Gai, Xiangyu Zhao, and Yongfeng Zhang. 2023 · 2023
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Fast Slate Policy Optimization: Going Beyond Plackett-Luce
Otmane Sakhi, David Rohde, and Nicolas Chopin. 2023 · 2023
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Take a Fresh Look at Recommender Systems from an Evaluation Standpoint. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (Taipei, Taiwan) (SIGIR ’23) . Association for Computing Machinery, New York, NY, USA, 2629–2638
Aixin Sun. 2023 · 2023
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Gymnasium
Mark Towers, Jordan K. Terry, Ariel Kwiatkowski, John U. Balis, Gianluca de Cola, Tristan Deleu, Manuel Goulão, Andreas Kallinteris, Arjun K.G., Markus Krimmel, Rodrigo Perez-Vicente, Andrea Pierré, Sander Schulhoff, Jun Jet Tai, Andrew Tan Jin Shen, and Omar G. Younis. 2023 · 2023
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Learning Robust Recommenders through Cross-Model Agreement. In Proceedings of the ACM Web Conference 2022 (Virtual Event, Lyon, France) (WWW ’22) . Association for Computing Machinery, New York, NY, USA, 2015–2025
Yu Wang, Xin Xin, Zaiqiao Meng, Joemon M. Jose, Fuli Feng, and Xiangnan He. 2022 · 2025
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