Andreu Vall, M. Quadrana, Markus Schedl, Gerhard Widmer,
"Order, Context and Popularity Bias in Next-song Recommendations"
, in International Journal of Multimedia Information Retrieval, 2019, ISSN: 2192-662X
Original Titel:
Order, Context and Popularity Bias in Next-song Recommendations
Sprache des Titels:
Englisch
Original Kurzfassung:
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 themost recent user interactions. This study focuses on themusic 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.
Sprache der Kurzfassung:
Englisch
Journal:
International Journal of Multimedia Information Retrieval