The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study

Dominik Kowald, Markus Schedl, Elisabeth Lex

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 pop-
ularity 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 recommen-
dation. Specifically, we investigate three user groups from the Last.fm
music platform that are categorized based on how much their listen-
ing 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 Aver-
age 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.