MusicBox: Personalized Music Recommendation Based on Cubic Analysis of Social Tags

—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 3-order tensors, which capture cubic (3way) 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 be...

• Type: Journal
• Year: 2010
• Where: TASLP
• Authors: Alexandros Nanopoulos, Dimitrios Rafailidis, Panagiotis Symeonidis, Yannis Manolopoulos
• Keywords: Music, Social Tagging Data, Social Tags, TASLP 2010