How much metadata do we need in music recommendation?: a subjective evaluation using preference sets

Abstract

Comunicació presentada a: 2th International Society for Music Information Retrieval Conference (ISMIR 2011) celebrat del 24 al 28 d'octubre de 2011 a Miami, EEUU.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.This research has been partially funded by the FI Grant of Generalitat de Catalunya (AGAUR) and the Buscamedia (CEN-20091026), Classical Planet (TSI-070100-2009-407, MITYC), and DRIMS (TIN2009-14247- C02-01, MICINN) projects