mlsanityData to 7 Oct 2026

User Fairness in Recommender Systems

arXiv 1807.06349 ↗First posted 17 Jul 2018cs.CY cs.IRcited by 20

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Abstract (arXiv)

Recent works in recommendation systems have focused on diversity in recommendations as an important aspect of recommendation quality. In this work we argue that the post-processing algorithms aimed at only improving diversity among recommendations lead to discrimination among the users. We introduce the notion of user fairness which has been overlooked in literature so far and propose measures to quantify it. Our experiments on two diversification algorithms show that an increase in aggregate diversity results in increased disparity among the users.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jul 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 5; 2023: 5; 2024: 2; 2025: 2; 2026: 3.

20 papers.

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  1. 2022Fairness in Graph Mining: A Surveycited by 76
  2. 2021User-oriented Fairness in Recommendationcited by 75
  3. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
  4. 2023UP5: Unbiased Foundation Model for Fairness-aware Recommendationcited by 38
  5. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  6. 2022Trustworthy Recommender Systemscited by 21
  7. 2023A Survey on Fairness-aware Recommender Systemscited by 14
  8. 2022Manifestations of Xenophobia in AI Systemscited by 4
  9. 2024Federated Fairness Analytics: Quantifying Fairness in Federated Learningcited by 4
  10. 2021Sampling a Near Neighbor in High Dimensions -- Who is the Fairest of Them All?cited by 3
  11. 2023Uncovering Bias in Personal Informaticscited by 3
  12. 2023FairRec: Fairness Testing for Deep Recommender Systemscited by 3
8 more
  1. 2023Metrics for popularity bias in dynamic recommender systemscited by 3
  2. 2025Stairway to Fairness: Connecting Group and Individual Fairnesscited by 3
  3. 2024UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systemscited by 2
  4. 2026Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learningcited by 2
  5. 2026Measuring Individual User Fairness with User Similarity and Effectiveness Disparitycited by 1
  6. 2022Towards User-Centered Metrics for Trustworthy AI in Immersive Cyberspacecited by 0
  7. 2025Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systemscited by 0
  8. 2026SRBench: A Comprehensive Benchmark for Sequential Recommendation with Large Language Modelscited by 0