The unfairness of popularity bias in music recommendation: A reproducibility study

Dominik Kowald*, Markus Schedl, Elisabeth Lex

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

Abstract

Research has shown that recommender systems are typically biased towards popular items, which leads to less popular items being underrepresented in recommendations. The recent work of Abdollahpouri et al. in the context of movie recommendations has shown that this popularity bias leads to unfair treatment of both long-tail items as well as users with little interest in popular items. In this paper, we reproduce the analyses of Abdollahpouri et al. in the context of music recommendation. Specifically, we investigate three user groups from the Last.fm music platform that are categorized based on how much their listening preferences deviate from the most popular music among all Last.fm users in the dataset: (i) low-mainstream users, (ii) medium-mainstream users, and (iii) high-mainstream users. In line with Abdollahpouri et al., we find that state-of-the-art recommendation algorithms favor popular items also in the music domain. However, their proposed Group Average Popularity metric yields different results for Last.fm than for the movie domain, presumably due to the larger number of available items (i.e., music artists) in the Last.fm dataset we use. Finally, we compare the accuracy results of the recommendation algorithms for the three user groups and find that the low-mainstreaminess group significantly receives the worst recommendations.

Originalspracheenglisch
TitelAdvances in Information Retrieval - 42nd European Conference on IR Research, ECIR 2020, Proceedings
Redakteure/-innenJoemon M. Jose, Emine Yilmaz, João Magalhães, Flávio Martins, Pablo Castells, Nicola Ferro, Mário J. Silva
Herausgeber (Verlag)Springer
Seiten35-42
Seitenumfang8
ISBN (Print)9783030454418
DOIs
PublikationsstatusVeröffentlicht - 1 Jan 2020
Veranstaltung42nd European Conference on IR Research, ECIR 2020 - Lisbon, Portugal
Dauer: 14 Apr 202017 Apr 2020

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band12036 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz42nd European Conference on IR Research, ECIR 2020
LandPortugal
OrtLisbon
Zeitraum14/04/2017/04/20

ASJC Scopus subject areas

  • !!Theoretical Computer Science
  • !!Computer Science(all)

Fingerprint Untersuchen Sie die Forschungsthemen von „The unfairness of popularity bias in music recommendation: A reproducibility study“. Zusammen bilden sie einen einzigartigen Fingerprint.

  • Dieses zitieren

    Kowald, D., Schedl, M., & Lex, E. (2020). The unfairness of popularity bias in music recommendation: A reproducibility study. in J. M. Jose, E. Yilmaz, J. Magalhães, F. Martins, P. Castells, N. Ferro, & M. J. Silva (Hrsg.), Advances in Information Retrieval - 42nd European Conference on IR Research, ECIR 2020, Proceedings (S. 35-42). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12036 LNCS). Springer. https://doi.org/10.1007/978-3-030-45442-5_5