How Much Metadata Do We Need in Music Recommendation? A Subjective Evaluation Using Preference Sets

In this work we consider distance-based approaches to music recommendation, relying on an explicit set of music tracks provided by the user as evidence of his/her music preferences. Firstly, we propose a purely content-based approach, working on low-level (timbral, temporal, and tonal) and inferred high-level semantic descriptions of music. Secondly, we consider its simple refinement by adding a minimum amount of genre metadata. We compare the proposed approaches with one content-based and three metadata-based baselines. As such, we consider content-based approach working on inferred semantic descriptors, a tag-based recommender exploiting artist tags, a commercial black-box recommender partially employing collaborative filtering information, and a simple genre-based random recommender. We conduct a listening experiment with 19 participants. The obtained results reveal that although the low-level/semantic content-based approach does not achieve the performance of the baseline working exclusively on the inferred semantic descriptors, the proposed refinement provides significant improvement in the listeners’ satisfaction comparable with metadata-based approaches, and surpasses these approaches by the number of novel relevant recommendations. We conclude that the proposed content-based approach refined by simple genre metadata is suited for music discovery not only in the long-tail but also within popular music items.