It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation

Abstract

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 that feedback loop to study 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 user interaction and re-training 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.

arXiv comment6 pages, 3 figures, conference short paper, to be published at RecSys 2024

References in corpus (8)

• Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions
• FA*IR: A Fair Top-k Ranking Algorithm
• A Survey on the Fairness of Recommender Systems
• How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
• Fairness in Information Access Systems
• LensKit for Python: Next-Generation Software for Recommender System Experiments
• Distributionally-Informed Recommender System Evaluation
• Unified Browsing Models for Linear and Grid Layouts

Cited by in corpus (2)

• Stairway to Fairness: Connecting Group and Individual Fairness
• User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation