Order, context and popularity bias in next-song recommendations

International Journal of Multimedia Information Retrieval - Tập 8 - Trang 101-113 - 2019
Andreu Vall1, Massimo Quadrana2, Markus Schedl1, Gerhard Widmer1,3
1Institute of Computational Perception, Johannes Kepler University Linz, Linz, Austria
2Pandora Media Inc., Oakland, USA
3Austrian Research Institute for Artificial Intelligence, Vienna, Austria

Tóm tắt

The availability of increasingly larger multimedia collections has fostered extensive research in recommender systems. Instead of capturing general user preferences, the task of next-item recommendation focuses on revealing specific session preferences encoded in the most recent user interactions. This study focuses on the music domain, particularly on the task of music playlist continuation, a paradigmatic case of next-item recommendation. While the accuracy achieved in next-song recommendations is important, in this work we shift our focus toward a deeper understanding of fundamental playlist characteristics, namely the song order, the song context and the song popularity, and their relation to the recommendation of playlist continuations. We also propose an approach to assess the quality of the recommendations that mitigates known problems of off-line experiments for music recommender systems. Our results indicate that knowing a longer song context has a positive impact on next-song recommendations. We find that the long-tailed nature of the playlist datasets makes simple and highly expressive playlist models appear to perform comparably, but further analysis reveals the advantage of using highly expressive models. Finally, our experiments suggest that the song order is not crucial to accurately predict next-song recommendations.

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International Journal of Multimedia Information Retrieval

Tập 8

101-113

Thông tin tác giả