  Music recommender systems have become a key technology supporting the access
to increasingly larger music catalogs in on-line music streaming services,
on-line music shops, and private collections. The interaction of users with
large music catalogs is a complex phenomenon researched from different
disciplines. We survey our works investigating the machine learning and data
mining aspects of hybrid music recommender systems (i.e., systems that
integrate different recommendation techniques). We proposed hybrid music
recommender systems based solely on data and robust to the so-called
"cold-start problem" for new music items, favoring the discovery of relevant
but non-popular music. We thoroughly studied the specific task of music
playlist continuation, by analyzing fundamental playlist characteristics, song
feature representations, and the relationship between playlists and the songs
therein.
