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

TL;DR

This paper introduces a music recommendation method that uses psychologically grounded acoustic features to improve recommendations for new songs, addressing the cold-start problem more effectively than traditional content-agnostic methods.

Contribution

It proposes leveraging low-level acoustic features based on music psychology to enhance content-aware collaborative filtering for music recommendation.

Findings

Addresses cold-start problem effectively

Uses a compact set of meaningful musical features

Improves recommendation accuracy on large-scale data

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.