Leveraging the structure of musical preference in content-aware music recommendation
This is an incremental improvement for music recommendation systems, enhancing content-aware approaches with interpretable features.
The paper tackled the cold-start problem in music recommendation by integrating psychologically-grounded musical preference factors (arousal, valence, depth) into a collaborative filtering framework, showing it effectively addresses the issue with a compact feature set.
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.