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Social tagging is becoming increasingly popular in music information retrieval (MIR). It allows users to tag music items like songs, albums, or artists. Social tags are valuable to MIR, because they comprise a multifaced source of information about genre, style, mood, users' opinion, or instrumentation. In this paper, we examine the problem of personalized music recommendation based on social tags. We propose the modeling of social tagging data with three-order tensors, which capture cubic (three-way) correlations between users-tags-music items. The discovery of latent structure in this model is performed with the Higher Order Singular Value Decomposition (HOSVD), which helps to provide accurate and personalized recommendations, i.e., adapted to the particular users' preferences. To address the sparsity that incurs in social tagging data and further improve the quality of recommendation, we propose to enhance the model with a tag-propagation scheme that uses similarity values computed between the music items based on audio features. As a result, the proposed model effectively combines both information about social tags and audio features. The performance of the proposed method is examined experimentally with real data from Last.fm. Our results indicate the superiority of the proposed approach compared to existing methods that suppress the cubic relationships that are inherent in social tagging data. Additionally, our results suggest that the combination of social tagging data with audio features is preferable than the sole use of the former.