  Playlist recommendation involves producing a set of songs that a user might
enjoy. We investigate this problem in three cold-start scenarios: (i) cold
playlists, where we recommend songs to form new personalised playlists for an
existing user; (ii) cold users, where we recommend songs to form new playlists
for a new user; and (iii) cold songs, where we recommend newly released songs
to extend users' existing playlists. We propose a flexible multitask learning
method to deal with all three settings. The method learns from user-curated
playlists, and encourages songs in a playlist to be ranked higher than those
that are not by minimising a bipartite ranking loss. Inspired by an equivalence
between bipartite ranking and binary classification, we show how one can
efficiently approximate an optimal solution of the multitask learning objective
by minimising a classification loss. Empirical results on two real playlist
datasets show the proposed approach has good performance for cold-start
playlist recommendation.
