Using social tags to infer context in hybrid music recommendation

Contextual factors can greatly influence users' decisions in selecting items, such as songs when listening to music. The goal of a context-aware recommender system is to adapt its recommendations not just to the general preferences of users, but also to the context in which users are seeking those recommendations. In the domain of music recommendation, the explicit contextual factors and their values might not be known to the system, a priori. Moreover, the contextual state of a user can be dynamic and change during an interaction with the system. In this paper, we present a hybrid context-aware recommender system which infers contextual information from the sequence of songs listened to or specified by a user and uses this information to produce context-aware recommendations. Our system mines popular tags for songs from social media Web sites and uses a topic modeling approach to learn latent topics representing various contexts. We then model each song as a set of latent topics capturing the general characteristics of that song. This representation is used to track and detect changes in user's choice of music, as reflected in a playlist of song sequence, and adjust the recommendations to better meet the current context of the user. Using our approach, the contextual information can be integrated with any traditional recommendation algorithm to produce context-aware recommendations. For our system, we designed and evaluated two hybrid methods. The first hybrid combines collaborative filtering and content-based recommendation techniques, and the second hybrid additionally incorporates information about pairwise song associations. Our evaluation results show that both the hybrid approach and the contextualization can enhance the performance of baseline music recommendation method.