A Hybrid Approach to Music Playlist Continuation Based on Playlist-Song Membership
Andreu Vall, Matthias Dorfer, Markus Schedl, Gerhard Widmer

TL;DR
This paper presents a hybrid music playlist continuation model based on playlist-song membership, capable of extending both profiled and non-profiled playlists, and recommending rare or unseen songs by analyzing feature vectors.
Contribution
The paper introduces a novel hybrid model that improves playlist continuation by leveraging playlist-song membership, overcoming limitations of pure collaborative filtering methods.
Findings
Performs comparably to state-of-the-art collaborative filtering on common tasks.
Can extend non-profiled playlists and recommend seldom or unseen songs.
Shows robustness in recommending rare or new songs.
Abstract
Automated music playlist continuation is a common task of music recommender systems, that generally consists in providing a fitting extension to a given playlist. Collaborative filtering models, that extract abstract patterns from curated music playlists, tend to provide better playlist continuations than content-based approaches. However, pure collaborative filtering models have at least one of the following limitations: (1) they can only extend playlists profiled at training time; (2) they misrepresent songs that occur in very few playlists. We introduce a novel hybrid playlist continuation model based on what we name "playlist-song membership", that is, whether a given playlist and a given song fit together. The proposed model regards any playlist-song pair exclusively in terms of feature vectors. In light of this information, and after having been trained on a collection of labeled…
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