Evaluating Hybrid Music Recommender Systems

Abstract

Taste in music is of highly subjective nature, making the recommending of music tracks a challenging research task. With TRecS, our live prototype system, we present a weighted hybrid recommender approach that amalgamates three diverse recommender techniques into one comprehensive score. Moreover, our system peppers the generated result list with recommendations based on a simple serendipity heuristic. This way, users can benefit from recommendations aligned with their current taste in music while gaining some exploratory diversification. An explanation feature helps the user understand the rationale behind each of the tracks being recommended to him. Empirical evaluations of the live system, based on an online evaluation, assess the overall recommendation quality as well as the impact of each of the three sub-recommenders.