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Feedback Loop and Bias Amplification in Recommender Systems

arXiv 2007.13019 ↗First posted 25 Jul 2020cs.IRcited by 89

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Abstract (arXiv)

Recommendation algorithms are known to suffer from popularity bias; a few popular items are recommended frequently while the majority of other items are ignored. These recommendations are then consumed by the users, their reaction will be logged and added to the system: what is generally known as a feedback loop. In this paper, we propose a method for simulating the users interaction with the recommenders in an offline setting and study the impact of feedback loop on the popularity bias amplification of several recommendation algorithms. We then show how this bias amplification leads to several other problems such as declining the aggregate diversity, shifting the representation of users' taste over time and also homogenization of the users experience. In particular, we show that the impact of feedback loop is generally stronger for the users who belong to the minority group.

Key facts. Cited by 89 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jul 2020.

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Citing papers per year: 2021: 9; 2022: 23; 2023: 17; 2024: 17; 2025: 16; 2026: 7.

89 papers.

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  1. 2023Recommender Systems with Generative Retrievalcited by 191
  2. 2023On Generative Agents in Recommendationcited by 88
  3. 2024Feedback Loops With Language Models Drive In-Context Reward Hackingcited by 70
  4. 2023Leveraging Large Language Models in Conversational Recommender Systemscited by 63
  5. 2023Towards Understanding the Interplay of Generative Artificial Intelligence and the Internetcited by 54
  6. 2023A Survey on Popularity Bias in Recommender Systemscited by 42
  7. 2021User-centered Evaluation of Popularity Bias in Recommender Systemscited by 38
  8. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  9. 2022Estimating and Penalizing Induced Preference Shifts in Recommender Systemscited by 29
  10. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  11. 2023Human-AI Coevolutioncited by 27
  12. 2022Preference Dynamics Under Personalized Recommendationscited by 25
38 more
  1. 2022Performative Reinforcement Learningcited by 25
  2. 2022StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Randomcited by 24
  3. 2022Fair ranking: a critical review, challenges, and future directionscited by 21
  4. 2024How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspectivecited by 21
  5. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Surveycited by 18
  6. 2021Correcting Exposure Bias for Link Recommendationcited by 17
  7. 2024Agentic Feedback Loop Modeling Improves Recommendation and User Simulationcited by 16
  8. 2024LLMs for User Interest Exploration in Large-scale Recommendation Systemscited by 15
  9. 2022Breaking Feedback Loops in Recommender Systems with Causal Inferencecited by 14
  10. 2021A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systemscited by 13
  11. 2022Experiments on Generalizability of User-Oriented Fairness in Recommender Systemscited by 13
  12. 2023Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendationscited by 12
  13. 2022Speciesist bias in AI -- How AI applications perpetuate discrimination and unfair outcomes against animalscited by 11
  14. 2024Characterizing and modeling harms from interactions with design patterns in AI interfacescited by 11
  15. 2024Accounting for AI and Users Shaping One Another: The Role of Mathematical Modelscited by 11
  16. 2025The Lock-in Hypothesis: Stagnation by Algorithmcited by 11
  17. 2021Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approachcited by 9
  18. 2022Diversified Recommendations for Agents with Adaptive Preferencescited by 9
  19. 2024Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutionscited by 9
  20. 2024EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systemscited by 8
  21. 2024Grounding Natural Language to SQL Translation with Data-Based Self-Explanationscited by 7
  22. 2024Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairnesscited by 6
  23. 2022Synthetic Data-Based Simulators for Recommender Systems: A Surveycited by 5
  24. 2023Online Recommendations for Agents with Discounted Adaptive Preferencescited by 5
  25. 2024Harm Mitigation in Recommender Systems under User Preference Dynamicscited by 5
  26. 2024Addressing Discretization-Induced Bias in Demographic Predictioncited by 4
  27. 2025Performative Policy Gradient: Optimality in Performative Reinforcement Learningcited by 4
  28. 2022Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AIcited by 3
  29. 2022Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagementcited by 3
  30. 2023Metrics for popularity bias in dynamic recommender systemscited by 3
  31. 2023Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approachcited by 3
  32. 2024Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systemscited by 3
  33. 2024Identifying Predictions That Influence the Future: Detecting Performative Concept Drift in Data Streamscited by 3
  34. 2025Diffusion Models in Recommendation Systems: A Surveycited by 3
  35. 2025AdaParse: An Adaptive Parallel PDF Parsing and Resource Scaling Enginecited by 3
  36. 2022Pessimistic Off-Policy Optimization for Learning to Rankcited by 2
  37. 2022Causal Structure Learning with Recommendation Systemcited by 2
  38. 2023Biases in Scholarly Recommender Systems: Impact, Prevalence, and Mitigationcited by 2

50 most cited of 89 All 89 (JSON)