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