https://doi.org/10.1140/epjds/s13688-021-00268-9
Regular Article
Support the underground: characteristics of beyond-mainstream music listeners
1
Know-Center GmbH, Graz, Austria
2
University of Innsbruck, Innsbruck, Austria
3
Utrecht University, Utrecht, The Netherlands
4
Johannes Kepler University Linz, Linz, Austria
5
Linz Institute of Technology AI Lab, Linz, Austria
6
Graz University of Technology, Graz, Austria
a
dkowald@know-center.at
f
elisabeth.lex@tugraz.at
Received:
7
August
2020
Accepted:
23
February
2021
Published online:
30
March
2021
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.
Key words: Music recommender systems / Acoustic features / Last.fm / Clustering / User modeling / Fairness / Popularity bias / Beyond-mainstream users
© The Author(s) 2021
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.