Support the Underground: Characteristics of Beyond-Mainstream Music Listeners

TL;DR

This paper investigates the unique characteristics of beyond-mainstream music listeners, analyzes how these traits affect recommendation quality, and provides insights for developing better personalized music recommender systems.

Contribution

It introduces a novel dataset of beyond-mainstream music listeners, identifies subgroups with distinct preferences and demographics, and evaluates recommendation algorithms across these groups.

Findings

Four subgroups of beyond-mainstream listeners with different preferences and demographics

Significant differences in recommendation quality across subgroups

Openness to diverse music correlates with higher recommendation accuracy

Abstract

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.