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In this paper, we analyze a large dataset of user-generated music listeningevents from Last.fm, focusing on users aged 6 to 18 years. Our contribution istwo-fold. First, we study the music genre preferences of this young user groupand analyze these preferences for homogeneity within more fine-grained agegroups and with respect to gender and countries. Second, we investigate theperformance of a collaborative filtering recommender when tailoring musicrecommendations to different age groups. We find that doing so improvesperformance for all user groups up to 18 years, but decreases performance foradult users aged 19 years and older.

Online Music Listening Culture of Kids and Adolescents: Listening Analysis and Music Recommendation Tailored to the Young