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Leveraging the structure of musical preference in content-aware music recommendation

Abstract : State-of-the-art music recommendation systems are based on collaborative filtering, which predicts a user's interest from his listening habits and similarities with other users' profiles. These approaches are agnostic to the song content, and therefore face the cold-start problem: they cannot recommend novel songs without listening history. To tackle this issue, content-aware recommendation incorporates information about the songs that can be used for recommending new items. Most methods falling in this category exploit either user-annotated tags, acoustic features or deeply-learned features. Consequently, these content features do not have a clear musical meaning, thus they are not necessarily relevant from a musical preference perspective. In this work, we propose instead to leverage a model of musical preference which originates from the field of music psychology. From low-level acoustic features we extract three factors (arousal, valence and depth), which have been shown appropriate for describing musical taste. Then we integrate those into a collaborative filtering framework for content-aware music recommendation. Experiments conducted on large-scale data show that this approach is able to address the cold-start problem, while using a compact and meaningful set of musical features.
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Contributor : Paul Magron <>
Submitted on : Tuesday, February 9, 2021 - 4:56:49 PM
Last modification on : Wednesday, June 9, 2021 - 10:00:28 AM


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  • HAL Id : hal-03049798, version 2
  • ARXIV : 2010.10276


Paul Magron, Cédric Févotte. Leveraging the structure of musical preference in content-aware music recommendation. IEEE International Conference on Acoustics, Speech and Signal Processing, Jun 2021, Toronto, Canada. ⟨hal-03049798v2⟩



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