mlsanityData to 7 Oct 2026

Human Interaction with Recommendation Systems

arXiv 1703.00535 ↗First posted 1 Mar 2017stat.ML cs.LGcited by 15

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Abstract (arXiv)

Many recommendation algorithms rely on user data to generate recommendations. However, these recommendations also affect the data obtained from future users. This work aims to understand the effects of this dynamic interaction. We propose a simple model where users with heterogeneous preferences arrive over time. Based on this model, we prove that naive estimators, i.e. those which ignore this feedback loop, are not consistent. We show that consistent estimators are efficient in the presence of myopic agents. Our results are validated using extensive simulations.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Mar 2017.

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Citing papers per year: 2021: 4; 2022: 7; 2023: 3; 2024: 1.

15 papers.

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  1. 2022Data Feedback Loops: Model-driven Amplification of Dataset Biasescited by 59
  2. 2022Preference Dynamics Under Personalized Recommendationscited by 25
  3. 2022Breaking Feedback Loops in Recommender Systems with Causal Inferencecited by 14
  4. 2021T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systemscited by 12
  5. 2024Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutionscited by 9
  6. 2021Correcting the User Feedback-Loop Bias for Recommendation Systemscited by 5
  7. 2022In the Eye of the Beholder: Robust Prediction with Causal User Modelingcited by 5
  8. 2022Incentive-Aware Recommender Systems in Two-Sided Marketscited by 5
  9. 2022Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagementcited by 3
  10. 2023Decongestion by Representation: Learning to Improve Economic Welfare in Marketplacescited by 3
  11. 2021Synthetic Data and Simulators for Recommendation Systems: Current State and Future Directionscited by 2
  12. 2023Counterfactual Augmentation for Multimodal Learning Under Presentation Biascited by 1
3 more
  1. 2021Bandit based centralized matching in two-sided markets for peer to peer lendingcited by 0
  2. 2022One-shot Machine Teaching: Cost Very Few Examples to Converge Fastercited by 0
  3. 2023DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loopscited by 0