Support the underground: characteristics of beyond-mainstream music listeners

Dominik Kowald,Elisabeth Lex,Markus Schedl,Christine Bauer,Peter Muellner,Eva Zangerle

doi:10.1140/epjds/s13688-021-00268-9

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.

Highlights

In the digital era, users have access to continually increasing amounts of music via music streaming services such as Spotify and Last.fm
Users with different preferences and needs can be targeted in various ways with the Present work In the paper at hand, we focus on music consumers who listen to music beyond the mainstream in the music streaming platform Last.fm
We identify more than 2000 beyond-mainstream music listeners on the Last.fm platform and enrich their listening profiles with acoustic features and genres of music tracks listened to (Sects. 3.1–3.4)

Summary

Introduction

Users have access to continually increasing amounts of music via music streaming services such as Spotify and Last.fm. Music recommender systems have become an essential means to help users deal with content and choice overload as they assist users in searching, sorting, and filtering these extensive music collections. It is a widely-known problem that recommender systems (and those based on collaborative filtering, in particular) are prone to popularity bias, which leads to the behavior that long-tail items (i.e., items with few user interactions) have little chance being recommended. This phenomenon is present across different application domains such as movies [2] or music [3]

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Results

Conclusion