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

Dominik Kowald*, Markus Schedl, Elisabeth Lex

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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 music platform that are categorized based on how much their listening preferences deviate from the most popular music among all 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 than for the movie domain, presumably due to the larger number of available items (i.e., music artists) in the 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.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 42nd European Conference on IR Research, ECIR 2020, Proceedings
EditorsJoemon M. Jose, Emine Yilmaz, João Magalhães, Flávio Martins, Pablo Castells, Nicola Ferro, Mário J. Silva
Number of pages8
ISBN (Print)9783030454418
Publication statusPublished - 1 Jan 2020
Event42nd European Conference on IR Research: ECIR 2020 - Virtuell, Lisbon, Portugal
Duration: 14 Apr 202017 Apr 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12036 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference42nd European Conference on IR Research
CityVirtuell, Lisbon
Internet address


  • Algorithmic fairness
  • Item popularity
  • Music recommendation
  • Popularity bias
  • Recommender systems
  • Reproducibility

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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