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Ternary Semantic Analysis of Social Tags for Personalized Music Recommendation

Social tagging is the process by which many users add metadata in the form of keywords, to annotate information items. In case of music, the annotated items can be songs, artists, albums. Current music recommenders which employ social tagging to improve the music recommendation, fail to always provide appropriate item recommendations, because: (i) users may have different interests for a musical item, and (ii) musical items may have multiple facets. In this paper, we propose an approach that tackles the problem of the multimodal use of music. We develop a unified framework, represented by a 3-order tensor, to model altogether users, tags, and items. Then, we recommend musical items according to users multimodal perception of music, by performing latent semantic analysis and dimensionality reduction using the Higher Order Singular Value Decomposition technique. We experimentally evaluate the proposed method against two state-of-the-art recommendations algorithms using real Last.fm data. Our results show significant improvements in terms of effectiveness measured through recall/precision.