Automatic playlist continuation using subprofile-aware diversification

The ACM RecSys Challenge 2018 involves the task of automatic playlist continuation (APC), aiming to help users to create and extend their own music playlists. In this paper, we explain teamrozik's approach to the Challenge. Our approach to APC is twofold: Cold-Start-APC for short playlists and SPAD-APC for other playlists. Cold-Start-APC is a rudimentary popularity-based recommender. SPAD-APC treats playlists as if they were user profiles. It builds an implicit matrix factorization model to generate initial recommendations. But it re-ranks those recommendations using SubProfile-Aware Diversification (SPAD), which is a personalized intent-aware diversification method. The SPAD re-ranking method aims to ensure that the final set of recommendations covers different interests or tastes in the playlists of the users, which we refer to as subprofiles. We show that such subprofiles do exist within playlists and we show that the SPAD method achieves higher precision than matrix factorization alone.