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In this paper, we introduce new formal methods and provide empirical evidence to highlight a unique safety concern prevalent in reinforcement learning (RL)-based recommendation algorithms -- 'user tampering.' User tampering is a situation where an RL-based recommender system may manipulate a media user's opinions through its suggestions as part of a policy to maximize long-term user engagement.
Markov decision processes
Martin L Puterman. 1990 · 1990
Earlier work this paper cites.
Decision-Theoretic Foundations for Causal Reasoning
David Heckerman and Ross Shachter. 1995 · 1995
Earlier work this paper cites.
An MDP-Based Recommender System
Guy Shani, David Heckerman, and Ronen Brafman. 2005 · 2005
Earlier work this paper cites.
Usage-Based Web Recommendations: A Reinforcement Learning Approach. In Proceedings of the 2007 ACM Conference on Recommender Systems (Minneapolis, MN, USA) (RecSys ’07) . Association for Computing Machinery, New York, NY, USA, 113–120
Nima Taghipour, Ahmad Kardan, and Saeed Shiry Ghidary. 2007 · 2007
Earlier work this paper cites.
A Hybrid Web Recommender System Based on Q-Learning. In Proceedings of the 2008 ACM Symposium on Applied Computing (Fortaleza, Ceara, Brazil) (SAC ’08) . Association for Computing Machinery, New York, NY, USA, 1164–1168
Nima Taghipour and Ahmad Kardan. 2008 · 2008
Earlier work this paper cites.
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
Earlier work this paper cites.
Personalized News Recommendation Based on Click Behavior. In Proceedings of the 15th International Conference on Intelligent User Interfaces (Hong Kong, China) (IUI ’10) . Association for Computing Machinery, New York, NY, USA, 31–40
Jiahui Liu, Peter Dolan, and Elin Rønby Pedersen. 2010 · 2010
Earlier work this paper cites.
Personalized News Recommendation Based on Collaborative Filtering. In 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology , Vol. 1. 437–441
Florent Garcin, Kai Zhou, Boi Faltings, and Vincent Schickel. 2012 · 2012
Earlier work this paper cites.
Recommender systems survey
Jesus Bobadilla, Fernando Ortega, A. Hernando, and A. Gutiérrez. 2013 · 2013
Earlier work this paper cites.
Ensemble Contextual Bandits for Personalized Recommendation. In Proceedings of the 8th ACM Conference on Recommender Systems (Foster City, Silicon Valley, California, USA) (RecSys ’14) . Association for Computing Machinery, New York, NY, USA, 73–80
Liang Tang, Yexi Jiang, Lei Li, and Tao Li. 2014 · 2014
Earlier work this paper cites.
Content-Based Collaborative Filtering for News Topic Recommendation. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (Austin, Texas) (AAAI’15) . AAAI Press, 217–223
Zhongqi Lu, Zhicheng Dou, Jianxun Lian, Xing Xie, and Qiang Yang. 2015 · 2015
Earlier work this paper cites.
Personalized Recommendation via Parameter-Free Contextual Bandits. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (Santiago, Chile) (SIGIR ’15) . Association for Computing Machinery, New York, NY, USA, 323–332
Liang Tang, Yexi Jiang, Lei Li, Chunqiu Zeng, and Tao Li. 2015 · 2015
Earlier work this paper cites.
Social Media Recommender Systems: Review and Open Research Issues
Anitha Anandhan, Liyana Shuib, Maizatul Akmar Ismail, and Ghulam Mujtaba. 2018 · 2018
Cited alongside, same era.
Exposure to opposing views on social media can increase political polarization
Christopher A. Bail, Lisa P. Argyle, Taylor W. Brown, John P. Bumpus, Haohan Chen, M. B. Fallin Hunzaker, Jaemin Lee, Marcus Mann, Friedolin Merhout, and Alexander Volfovsky. 2018 · 2018
Cited alongside, same era.
Horizon: Facebook’s Open Source Applied Reinforcement Learning Platform
Jason Gauci, Edoardo Conti, Yitao Liang, Kittipat Virochsiri, Yuchen He, Zachary Kaden, Vivek Narayanan, and Xiaohui Ye. 2018 · 2018
Cited alongside, same era.
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
Feng Liu, Ruiming Tang, Xutao Li, Yunming Ye, Haokun Chen, Huifeng Guo, and Yuzhou Zhang. 2018 · 2018
Cited alongside, same era.
DRN: A Deep Reinforcement Learning Framework for News Recommendation. In Proceedings of the 2018 World Wide Web Conference (Lyon, France) (WWW ’18) . International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 167–176
FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms. In Proceedings of The Web Conference 2020 (Taipei, Taiwan) (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
Later among the works it cites.
Toward Social Media Content Recommendation Integrated with Data Science and Machine Learning Approach for E-Learners
Zeinab Shahbazi and Yung Cheol Byun. 2020 · 2020
Later among the works it cites.
What are you optimizing for? Aligning Recommender Systems with Human Values. In Participatory Approaches to Machine Learning . International Conference on Machine Learning Workshop
Jonathan Stray, Steven Adler, and Dylan Hadfield-Menell. 2020 · 2020
Later among the works it cites.
Breaking the Social Media Prism: How to Make our Platforms Less Polarizing
Christopher A. Bail. 2021 · 2021
Closest in time.
Estimating and Penalizing Preference Shift in Recommender Systems
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Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, and Zhenhui Li. 2018 · 2018
Cited alongside, same era.
Degenerate Feedback Loops in Recommender Systems. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (Honolulu, HI, USA) (AIES ’19) . Association for Computing Machinery, New York, NY, USA, 383–390
Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, and Pushmeet Kohli. 2019 · 2019
Cited alongside, same era.
Aligning Daily Activities with Personality: Towards a Recommender System for Improving Wellbeing. In Proceedings of the 13th ACM Conference on Recommender Systems (Copenhagen, Denmark) (RecSys ’19) . Association for Computing Machinery, New York, NY, USA, 368–372
Mohammed Khwaja, Miquel Ferrer, Jesus Omana Iglesias, A. Aldo Faisal, and Aleksandar Matic. 2019 · 2019
Cited alongside, same era.
Multistakeholder recommendation: Survey and research directions
Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, and Luiz Pizzato. 2020 · 2020
Cited alongside, same era.
Pitfalls of Learning a Reward Function Online. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20 , Christian Bessiere (Ed.). International Joint Conferences on Artificial Intelligence Organization, 1592–1600
Stuart Armstrong, Jan Leike, Laurent Orseau, and Shane Legg. 2020 · 2020
Cited alongside, same era.
Hidden Incentives for Auto-Induced Distributional Shift
David Krueger, Tegan Maharaj, and Jan Leike. 2020 · 2020
Cited alongside, same era.
Reinforcement Learning-based Product Delivery Frequency Control
Yang Liu, Zhengxing Chen, Kittipat Virochsiri, Juan Wang, Jiahao Wu, and Feng Liang. 2020 · 2020
Cited alongside, same era.
Recommender systems and their ethical challenges
Silvia Milano, Mariarosaria Taddeo, and Luciano Floridi. 2020 · 2020
Cited alongside, same era.
Micah Carroll, Dylan Hadfield-Menell, Stuart Russell, and Anca Dragan. 2021 · 2021
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Agent Incentives: A Causal Perspective
Tom Everitt, Ryan Carey, Eric D. Langlois, Pedro A. Ortega, and Shane Legg. 2021a · 2021
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Reward tampering problems and solutions in reinforcement learning: A causal influence diagram perspective
Tom Everitt, Marcus Hutter, Ramana Kumar, and Victoria Krakovna. 2021b · 2021
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Filter Bubbles and the Future of Artificial Intelligence
Stuart J. Russell. 2019a · 2021
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Reinforcement Learning Based Recommender Systems: A Survey
M. Mehdi Afsar, Trafford Crump, and Behrouz Far. 2022 · 2022
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Path-Specific Objectives for Safer Agent Incentives
Sebastian Farquhar, Ryan Carey, and Tom Everitt. 2022 · 2022
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Online Context-Aware Recommendation with Time Varying Multi-Armed Bandit. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (KDD ’16) . Association for Computing Machinery, New York, NY, USA, 2025–2034
Chunqiu Zeng, Qing Wang, Shekoofeh Mokhtari, and Tao Li. 2016 · 2034
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