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As recommender systems are prone to various biases, mitigation approaches are needed to ensure that recommendations are fair to various stakeholders. One particular concern in music recommendation is artist gender fairness. Recent work has shown that the gender imbalance in the sector translates to the output of music recommender systems, creating a feedback loop that can reinforce gender biases over time. In this work, we examine whether algorithmic strategies or user behavior are a greater contributor to ongoing improvement (or loss) in fairness as models are repeatedly re-trained on new user feedback data. We simulate this repeated process to investigate the effects of ranking strategies and user choice models on gender fairness metrics. We find re-ranking strategies have a greater effect than user choice models on recommendation fairness over time.
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Reproduction code for "User and Recommender Behavior Over Time" at FairUMAP 2025
This repository contains the reproduction code for "User and Recommender Behavior Over Time" paper at FairUMAP 2025: Samira Vaez Barenji, Sushobhan Parajuli, Michael D. Ekstrand. 2025. User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation. In Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization (UMAP Adjunct ’25). ACM. DOI: 10.1145/3708319.3733710. arXiv: 2505.04518 [cs.IR].
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- Award ID(s):
- 2415042
- PAR ID:
- 10654843
- Publisher / Repository:
- Zenodo
- Date Published:
- Format(s):
- Medium: X
- Right(s):
- MIT License
- Sponsoring Org:
- National Science Foundation
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