  Automated music playlist generation is a specific form of music
recommendation. Generally stated, the user receives a set of song suggestions
defining a coherent listening session. We hypothesize that the best way to
convey such playlist coherence to new recommendations is by learning it from
actual curated examples, in contrast to imposing ad hoc constraints.
Collaborative filtering methods can be used to capture underlying patterns in
hand-curated playlists. However, the scarcity of thoroughly curated playlists
and the bias towards popular songs result in the vast majority of songs
occurring in very few playlists and thus being poorly recommended. To overcome
this issue, we propose an alternative model based on a song-to-playlist
classifier, which learns the underlying structure from actual playlists while
leveraging song features derived from audio, social tags and independent
listening logs. Experiments on two datasets of hand-curated playlists show
competitive performance compared to collaborative filtering when sufficient
training data is available and more robust performance when recommending rare
and out-of-set songs. For example, both approaches achieve a recall@100 of
roughly 35% for songs occurring in 5 or more training playists, whereas the
proposed model achieves a recall@100 of roughly 15% for songs occurring in 4 or
less training playlists, compared to the 3% achieved by collaborative
filtering.
