Although recommender systems (RSs) play a crucial role in our society, previous studies have revealed that the performance of RSs may considerably differ between groups of individuals with different characteristics or from different demographics. In this case, a RS is considered to be unfair when it does not perform equally well for different groups of users. Considering the importance of RSs in the distribution and consumption of musical content worldwide, a careful evaluation of fairness in the context of music RSs is crucial. To this end, we first introduce LFM-2b, a novel large-scale real-world dataset of music listening records, comprising a subset to investigate bias of RSs regarding users’ demographics. We then define a notion of fairness based on the performance gap of a RS between the users with different demographics, and evaluate a variety of collaborative filtering algorithms in terms of accuracy and beyond-accuracy metrics to explore the fairness in the RS results toward a specific gender group. We observe the existence of significant discrepancies (unfairness) between the performance of algorithms across male and female user groups. Based on these discrepancies, we explore to what extent recommender algorithms lead to intensifying the underlying population bias in the final results. We also study the effect of a resampling strategy, commonly used as debiasing method , which yields slight improvements in the fairness measures of various algorithms while maintaining their accuracy and beyond-accuracy performance.

尽管推荐系统 (RS) 在我们的社会中发挥着至关重要的作用，但先前的研究表明，RS 的性能在具有不同特征或来自不同人口统计数据的个人群体之间可能会有很大差异。在这种情况下，当 RS对不同的用户组表现不佳时，它被认为是不公平的。考虑到 RS 在全球音乐内容的分发和消费中的重要性，仔细评估音乐 RS 背景下的公平性至关重要。为此，我们首先介绍LFM-2b，一种新颖的大规模真实世界音乐聆听记录数据集，包括一个子集，用于调查 RS 对用户人口统计数据的偏见。然后，我们根据具有不同人口统计特征的用户之间的 RS 性能差距来定义公平性概念，并根据准确性和超出准确性指标评估各种协同过滤算法，以探索 RS 结果中针对特定用户的公平性性别组。我们观察到男性和女性用户组的算法性能之间存在显着差异（不公平）。基于这些差异，我们探索了推荐算法在多大程度上导致最终结果中潜在的人口偏见加剧。我们还研究了重采样策略的效果，通常用作去偏差方法，