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

Information Retrieval;Machine Learning;Social and Information Networks