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