Quantitative User Perceptions of Music Recommendation List Diversity

Diversity is known to play an important role in recommender systems. However, its relationship to users and their satisfaction is not well understood, especially in the music domain. We present a user study: 92 participants were asked to evaluate personalized recommendation lists at varying levels of diversity. Recommendations were generated by two different collaborative filtering methods, and diversified in three different ways, one of which is a simple and novel method based on genre filtering. All diversified lists were recognised by users to be more diverse, and this diversification increased overall recommendation list satisfaction. Our simple filtering approach was also successful at tailoring diversity to some users. Within the collaborative filtering framework, however, we were not able to generate enough diversity to match all user preferences. Our results highlight the need to diversify in music recommendation lists, even when it comes at the cost of "accuracy".