mlsanityData to 7 Oct 2026

Recommendation Fairness: From Static to Dynamic

arXiv 2109.03150 ↗First posted 5 Sep 2021cs.IR cs.AI cs.LGcited by 10

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Abstract (arXiv)

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. Shouldn't research on the fairness of recommender systems follow the same trend from static evaluation and one-shot intervention to dynamic monitoring and non-stop control? In this paper, we portray the recent developments in recommender systems first and then discuss how fairness could be baked into the reinforcement learning techniques for recommendation. Moreover, we argue that in order to make further progress in recommendation fairness, we may want to consider multi-agent (game-theoretic) optimization, multi-objective (Pareto) optimization, and simulation-based optimization, in the general framework of stochastic games.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 46 of its 109 references that are in the map. First posted on arXiv 5 Sep 2021.

Builds on

46 of 109 references.

References · 46

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  2. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  3. 2017Neural Collaborative Filteringcited by 1,108
  4. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 774
  5. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
  6. 2020MOReL : Model-Based Offline Reinforcement Learningcited by 582
  7. 2019Introduction to Multi-Armed Banditscited by 475
  8. 2018Fairness of Exposure in Rankingscited by 189
  9. 2017Neural Factorization Machines for Sparse Predictive Analyticscited by 183
  10. 2018Delayed Impact of Fair Machine Learningcited by 183
  11. 2018Top-K Off-Policy Correction for a REINFORCE Recommender Systemcited by 181
  12. 2019Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Searchcited by 137
34 more
  1. 2020Neural Collaborative Filtering vs. Matrix Factorization Revisitedcited by 123
  2. 2021Causal Intervention for Leveraging Popularity Bias in Recommendationcited by 116
  3. 2018The Social Cost of Strategic Classificationcited by 105
  4. 2017Runaway Feedback Loops in Predictive Policingcited by 97
  5. 2016Fairness in Reinforcement Learningcited by 93
  6. 2017Beyond Parity: Fairness Objectives for Collaborative Filteringcited by 93
  7. 2020Game-Theoretic Multiagent Reinforcement Learningcited by 92
  8. 2021Representation Matters: Offline Pretraining for Sequential Decision Makingcited by 90
  9. 2019Achieving Fairness in the Stochastic Multi-armed Bandit Problemcited by 77
  10. 2020FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platformscited by 77
  11. 2021Towards Long-term Fairness in Recommendationcited by 77
  12. 2017IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Modelscited by 75
  13. 2018The Disparate Effects of Strategic Manipulationcited by 75
  14. 2020Fairness-Aware Explainable Recommendation over Knowledge Graphscited by 74
  15. 2018Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learningcited by 71
  16. 2019Combinatorial Sleeping Bandits with Fairness Constraintscited by 68
  17. 2019The Unfairness of Popularity Bias in Recommendationcited by 68
  18. 2020Controlling Fairness and Bias in Dynamic Learning-to-Rankcited by 67
  19. 2020Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewardscited by 60
  20. 2020How Do Fair Decisions Fare in Long-term Qualification?cited by 59
  21. 2019Algorithms for Fairness in Sequential Decision Makingcited by 53
  22. 2020Self-Supervised Reinforcement Learning for Recommender Systemscited by 49
  23. 2019Causal Modeling for Fairness in Dynamical Systemscited by 46
  24. 2019Learning Fairness in Multi-Agent Systemscited by 44
  25. 2019Fair Contextual Multi-Armed Bandits: Theory and Experimentscited by 41
  26. 2020Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learningcited by 40
  27. 2018From Fair Decision Making to Social Equalitycited by 36
  28. 2020The Connection Between Popularity Bias, Calibration, and Fairness in Recommendationcited by 35
  29. 2021TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providerscited by 31
  30. 2018Fair Algorithms for Learning in Allocation Problemscited by 29
  31. 2020Jointly Learning to Recommend and Advertisecited by 27
  32. 2021RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystemscited by 21
  33. 2019Dynamic Modeling and Equilibria in Fair Decision Makingcited by 8
  34. 2021Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learningcited by 7

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Cited by year

Citing papers per year: 2022: 5; 2023: 3; 2024: 2.

10 papers.

  1. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  2. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  3. 2022Survey on Fair Reinforcement Learning: Theory and Practicecited by 14
  4. 2023A Survey on Fairness-aware Recommender Systemscited by 14
  5. 2024The Pursuit of Fairness in Artificial Intelligence Models: A Surveycited by 13
  6. 2023Dynamic fairness-aware recommendation through multi-agent social choicecited by 9
  7. 2024Fairness in Reinforcement Learning: A Surveycited by 9
  8. 2023Fairness and Sequential Decision Making: Limits, Lessons, and Opportunitiescited by 5
  9. 2022Achievement and Fragility of Long-term Equitabilitycited by 0
  10. 2022Fair Machine Learning in Healthcare: A Reviewcited by 0