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Automated music playlist continuation is a common task of music recommendersystems, that generally consists in providing a fitting extension to a givenplaylist. Collaborative filtering models, that extract abstract patterns fromcurated music playlists, tend to provide better playlist continuations thancontent-based approaches. However, pure collaborative filtering models have atleast one of the following limitations: (1) they can only extend playlistsprofiled at training time; (2) they misrepresent songs that occur in very fewplaylists. We introduce a novel hybrid playlist continuation model based onwhat we name "playlist-song membership", that is, whether a given playlist anda given song fit together. The proposed model regards any playlist-song pairexclusively in terms of feature vectors. In light of this information, andafter having been trained on a collection of labeled playlist-song pairs, theproposed model decides whether a playlist-song pair fits together or not.Experimental results on two datasets of curated music playlists show that theproposed playlist continuation model compares to a state-of-the-artcollaborative filtering model in the ideal situation of extending playlistsprofiled at training time and where songs occurred frequently in trainingplaylists. In contrast to the collaborative filtering model, and as a result ofits general understanding of the playlist-song pairs in terms of featurevectors, the proposed model is additionally able to (1) extend non-profiledplaylists and (2) recommend songs that occurred seldom or never intraining~playlists.

A Hybrid Approach to Music Playlist Continuation Based on Playlist-Song Membership