mlsanityData to 14 Oct 2026

Metrics for popularity bias in dynamic recommender systems

arXiv 2310.08455 ↗First posted 12 Oct 2023cs.CY cs.AIcited by 3

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Abstract (arXiv)

Albeit the widespread application of recommender systems (RecSys) in our daily lives, rather limited research has been done on quantifying unfairness and biases present in such systems. Prior work largely focuses on determining whether a RecSys is discriminating or not but does not compute the amount of bias present in these systems. Biased recommendations may lead to decisions that can potentially have adverse effects on individuals, sensitive user groups, and society. Hence, it is important to quantify these biases for fair and safe commercial applications of these systems. This paper focuses on quantifying popularity bias that stems directly from the output of RecSys models, leading to over recommendation of popular items that are likely to be misaligned with user preferences. Four metrics to quantify popularity bias in RescSys over time in dynamic setting across different sensitive user groups have been proposed. These metrics have been demonstrated for four collaborative filtering based RecSys algorithms trained on two commonly used benchmark datasets in the literature. Results obtained show that the metrics proposed provide a comprehensive understanding of growing disparities in treatment between sensitive groups over time when used conjointly.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 18 of its 73 references that are in the map. First posted on arXiv 12 Oct 2023.

Builds on

18 of 73 references.

References · 18

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  2. 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 240
  3. 2022A Survey on the Fairness of Recommender Systemscited by 102
  4. 2018A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indicescited by 97
  5. 2020Feedback Loop and Bias Amplification in Recommender Systemscited by 90
  6. 2019The Unfairness of Popularity Bias in Recommendationcited by 70
  7. 2021User-centered Evaluation of Popularity Bias in Recommender Systemscited by 38
  8. 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
  9. 2019The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Studycited by 24
  10. 2020Multi-sided Exposure Bias in Recommendationcited by 23
  11. 2021Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendationcited by 23
  12. 2018User Fairness in Recommender Systemscited by 21
6 more
  1. 2018A Study of Position Bias in Digital Library Recommender Systemscited by 13
  2. 2022Quantifying and Mitigating Popularity Bias in Conversational Recommender Systemscited by 11
  3. 2022Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systemscited by 8
  4. 2020Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender Systemcited by 6
  5. 2017Use of Deep Learning in Modern Recommendation System: A Summary of Recent Workscited by 5
  6. 2020Quantifying the Effects of Recommendation Systemscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Adaptive Quality-Diversity Trade-offs for Large-Scale Batch Recommendationcited by 0
  2. 2026Principled Synthetic Data Enables the First Scaling Laws for LLMs in Recommendationcited by 0
  3. 2026Aligning Recommendations with User Popularity Preferencescited by 0