mlsanityData to 7 Oct 2026

How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

arXiv 1710.11214 ↗First posted 30 Oct 2017cs.CY cs.LG stat.MLcited by 116

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Abstract (arXiv)

Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users already exposed to algorithmic recommendations; this creates a pernicious feedback loop. Using simulations, we demonstrate how using data confounded in this way homogenizes user behavior without increasing utility.

Key facts. Cited by 116 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Oct 2017.

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Citing papers per year: 2021: 18; 2022: 25; 2023: 22; 2024: 14; 2025: 13; 2026: 24.

116 papers.

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  1. 2023Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedbackcited by 86
  2. 2024Feedback Loops With Language Models Drive In-Context Reward Hackingcited by 70
  3. 2022Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluationcited by 59
  4. 2022Data Feedback Loops: Model-driven Amplification of Dataset Biasescited by 59
  5. 2022Estimating and Penalizing Induced Preference Shifts in Recommender Systemscited by 29
  6. 2022Preference Dynamics Under Personalized Recommendationscited by 25
  7. 2022Performative Reinforcement Learningcited by 25
  8. 2023Long-Term Fairness with Unknown Dynamicscited by 25
  9. 2025iAgent: LLM Agent as a Shield between User and Recommender Systemscited by 23
  10. 2022Fair ranking: a critical review, challenges, and future directionscited by 21
  11. 2021Measuring Recommender System Effects with Simulated Userscited by 20
  12. 2021Unintended Selection: Persistent Qualification Rate Disparities and Interventionscited by 18
38 more
  1. 2021Correcting Exposure Bias for Link Recommendationcited by 17
  2. 2023Runtime Monitoring of Dynamic Fairness Propertiescited by 17
  3. 2024LLMs for User Interest Exploration in Large-scale Recommendation Systemscited by 15
  4. 2021Causal Incremental Graph Convolution for Recommender System Retrainingcited by 14
  5. 2022Breaking Feedback Loops in Recommender Systems with Causal Inferencecited by 14
  6. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  7. 2021A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systemscited by 13
  8. 2021T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systemscited by 12
  9. 2021Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectivenesscited by 12
  10. 2023Performative Recommendation: Diversifying Content via Strategic Incentivescited by 12
  11. 2023Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendationscited by 12
  12. 2023A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systemscited by 12
  13. 2025Disentangled Knowledge Tracing for Alleviating Cognitive Biascited by 12
  14. 2024Accounting for AI and Users Shaping One Another: The Role of Mathematical Modelscited by 11
  15. 2024Relevance meets Diversity: A User-Centric Framework for Knowledge Exploration through Recommendationscited by 11
  16. 2022Diversified Recommendations for Agents with Adaptive Preferencescited by 9
  17. 2023Human-AI Interactions and Societal Pitfallscited by 9
  18. 2021Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachabilitycited by 8
  19. 2022Towards Psychologically-Grounded Dynamic Preference Modelscited by 8
  20. 2023Online Matching: A Real-time Bandit System for Large-scale Recommendationscited by 8
  21. 2023Designing Long-term Group Fair Policies in Dynamical Systemscited by 8
  22. 2025AI Supply Chains: An Emerging Ecosystem of AI Actors, Products, and Servicescited by 7
  23. 2026How Does Personalized Memory Shape LLM Behavior? Benchmarking Rational Preference Utilization in Personalized Assistantscited by 7
  24. 2024Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairnesscited by 6
  25. 2021Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Samplingcited by 5
  26. 2021Addressing the Long-term Impact of ML Decisions via Policy Regretcited by 5
  27. 2021Correcting the User Feedback-Loop Bias for Recommendation Systemscited by 5
  28. 2022In the Eye of the Beholder: Robust Prediction with Causal User Modelingcited by 5
  29. 2022iFlipper: Label Flipping for Individual Fairnesscited by 5
  30. 2024Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferencescited by 5
  31. 2021Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulationcited by 4
  32. 2021Adversarial Gradient Driven Exploration for Deep Click-Through Rate Predictioncited by 4
  33. 2023Disentangled Representation for Diversified Recommendationscited by 4
  34. 2024Beyond Item Dissimilarities: Diversifying by Intent in Recommender Systemscited by 4
  35. 2025Performative Policy Gradient: Optimality in Performative Reinforcement Learningcited by 4
  36. 2022Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AIcited by 3
  37. 2022Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagementcited by 3
  38. 2023Reusable Self-Attention Recommender Systems in Fashion Industry Applicationscited by 3

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