As online music platforms grow, music recommender systems play a vital role in helping users navigate and discover content within their vast musical databases. At odds with this larger goal, is the presence of popularity bias, which causes algorithmic systems to favor mainstream content over, potentially more relevant, but niche items. In this work we explore the intrinsic relationship between music discovery and popularity bias. To mitigate this issue we propose a domain-aware, individual fairness-based approach which addresses popularity bias in graph neural network (GNNs) based recommender systems. Our approach uses individual fairness to reflect a ground truth listening experience, i.e., if two songs sound similar, this similarity should be reflected in their representations. In doing so, we facilitate meaningful music discovery that is robust to popularity bias and grounded in the music domain. We apply our BOOST methodology to two discovery based tasks, performing recommendations at both the playlist level and user level. Then, we ground our evaluation in the cold start setting, showing that our approach outperforms existing fairness benchmarks in both performance and recommendation of lesser-known content. Finally, our analysis explains why our proposed methodology is a novel and promising approach to mitigating popularity bias and improving the discovery of new and niche content in music recommender systems.

翻译：随着在线音乐平台的发展，音乐推荐系统在帮助用户导航和发现其海量音乐数据库中的内容方面发挥着关键作用。与这一宏大目标相悖的是流行度偏差的存在，这种偏差导致算法系统倾向于推荐主流内容，而忽略可能更具相关性但小众的项目。本文探讨了音乐发现与流行度偏差之间的内在关系。为缓解这一问题，我们提出了一种基于领域感知的个体公平性方法，用于解决基于图神经网络（GNN）的推荐系统中的流行度偏差。该方法利用个体公平性来反映真实的收听体验，即如果两首歌曲听起来相似，则这种相似性应体现在它们的表征中。通过这种方式，我们促进了有意义的音乐发现，使其对流行度偏差具有鲁棒性，并扎根于音乐领域。我们将BOOST方法应用于两个基于发现的任务，在歌单级别和用户级别上执行推荐。然后，我们在冷启动场景下进行评估，表明我们的方法在表现和推荐未知内容方面均优于现有的公平性基准。最后，我们的分析解释了为何所提出的方法是一种新颖且有前景的途径，可用于缓解流行度偏差并改善音乐推荐系统中新内容和小众内容的发现。