Music recommender systems have become an integral part of music streaming services such as Spotify and Last.fm to assist users navigating the extensive music collections offered by them. However, while music listeners interested in mainstream music are traditionally served well by music recommender systems, users interested in music beyond the mainstream (i.e., non-popular music) rarely receive relevant recommendations. In this paper, we study the characteristics of beyond-mainstream music and music listeners and analyze to what extent these characteristics impact the quality of music recommendations provided. Therefore, we create a novel dataset consisting of Last.fm listening histories of several thousand beyond-mainstream music listeners, which we enrich with additional metadata describing music tracks and music listeners. Our analysis of this dataset shows four subgroups within the group of beyond-mainstream music listeners that differ not only with respect to their preferred music but also with their demographic characteristics. Furthermore, we evaluate the quality of music recommendations that these subgroups are provided with four different recommendation algorithms where we find significant differences between the groups. Specifically, our results show a positive correlation between a subgroup’s openness towards music listened to by members of other subgroups and recommendation accuracy. We believe that our findings provide valuable insights for developing improved user models and recommendation approaches to better serve beyond-mainstream music listeners.

音乐推荐系统已成为 Spotify 和 Last.fm 等音乐流媒体服务不可或缺的一部分，以帮助用户浏览它们提供的大量音乐收藏。然而，虽然对主流音乐感兴趣的音乐听众传统上由音乐推荐系统很好地服务，但对主流以外的音乐（即，非流行音乐）感兴趣的用户很少收到相关推荐。在本文中，我们研究了非主流音乐和音乐听众的特征，并分析了这些特征在多大程度上影响了所提供音乐推荐的质量。因此，我们创建了一个新的数据集，其中包含数千个非主流音乐听众的 Last.fm 收听历史，我们用描述音乐曲目和音乐听众的额外元数据对其进行了丰富。我们对这个数据集的分析显示，超主流音乐听众组中的四个子组不仅在他们喜欢的音乐方面不同，而且在他们的人口特征方面也不同。此外，我们评估了音乐推荐的质量，这些子组提供了四种不同的推荐算法，我们发现这些组之间存在显着差异。具体来说，我们的结果表明，一个子组对其他子组成员听过的音乐的开放度与推荐准确性之间存在正相关。我们相信，我们的发现为开发改进的用户模型和推荐方法以更好地服务于非主流音乐听众提供了宝贵的见解。