研究发现，重排序策略比用户选择模型更能影响音乐推荐的性别公平性。
It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation
- 通过模拟用户交互与模型迭代，对比不同策略对公平性的影响。
- 重排序策略在长期推荐中显著提升性别公平性，优于用户选择模型。
- 适合关注算法公平性的推荐系统研究者和开发者参考。
由于推荐系统易受各类偏见影响，需采取缓解措施以确保对各利益相关方的公平性。音乐推荐中的艺术家性别公平性是一个重要关切。近期研究表明，该领域的性别失衡会传递至推荐系统输出，形成反馈循环，可能随时间加剧性别偏见。本文通过模拟用户交互与模型再训练，探究重排序策略与用户选择模型对性别公平性指标的影响。结果显示，重排序策略对长期推荐公平性的改善作用大于用户选择模型。
原文摘要 · Abstract (English)
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.
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