Analyzing item popularity bias of music recommender systems: Are different genders equally affected?

Oleg Lesota, Alessandro Melchiorre, Navid Rekabsaz, Stefan Brandl, Dominik Kowald, Elisabeth Lex, Markus Schedl*

*Korrespondierende/r Autor/-in für diese Arbeit

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


Several studies have identified discrepancies between the popularity of items in user profiles and the corresponding recommendation lists. Such behavior, which concerns a variety of recommendation algorithms, is referred to as popularity bias. Existing work predominantly adopts simple statistical measures, such as the difference of mean or median popularity, to quantify popularity bias. Moreover, it does so irrespective of user characteristics other than the inclination to popular content. In this work, in contrast, we propose to investigate popularity differences (between the user profile and recommendation list) in terms of median, a variety of statistical moments, as well as similarity measures that consider the entire popularity distributions (Kullback-Leibler divergence and Kendall's t rank-order correlation). This results in a more detailed picture of the characteristics of popularity bias. Furthermore, we investigate whether such algorithmic popularity bias affects users of different genders in the same way. We focus on music recommendation and conduct experiments on the recently released standardized LFM-2b dataset, containing listening profiles of users. We investigate the algorithmic popularity bias of seven common recommendation algorithms (five collaborative filtering and two baselines). Our experiments show that (1) the studied metrics provide novel insights into popularity bias in comparison with only using average differences, (2) algorithms less inclined towards popularity bias amplification do not necessarily perform worse in terms of utility (NDCG), (3) the majority of the investigated recommenders intensify the popularity bias of the female users.

TitelRecSys 2021 - 15th ACM Conference on Recommender Systems
Herausgeber (Verlag)Association of Computing Machinery
ISBN (elektronisch)9781450384582
PublikationsstatusVeröffentlicht - 13 Sept. 2021
Veranstaltung15th ACM Conference on Recommender Systems: RECSYS 2021 - Amsterdam, Niederlande
Dauer: 27 Sept. 20211 Okt. 2021


Konferenz15th ACM Conference on Recommender Systems
KurztitelRECSYS 2021

ASJC Scopus subject areas

  • Steuerungs- und Systemtechnik
  • Computernetzwerke und -kommunikation
  • Hardware und Architektur


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