A probabilistic model for music recommendation considering audio features

Abstract

In order to make personalized recommendations, many collaborative music recommender systems (CMRS) focused on capturing precise similarities among users or items based on user historical ratings. Despite the valuable information from audio features of music itself, however, few studies have investigated how to directly extract and utilize information from music for personalized recommendation in CMRS. In this paper, we describe a CMRS based on our proposed item-based probabilistic model, where items are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. By utilizing audio features, this model provides a way to alleviate three well-known challenges in collaborative recommender systems: user bias, non-association, and cold start problems in capturing accurate similarities among items. Experiments on a real-world data set illustrate that the audio information of music is quite useful and our system is feasible to integrate it for better personalized recommendation.