Construction of network using heterogeneous social metadata for music video recommendation

A novel method to construct a network based on heterogeneous features obtained from music videos and social metadata for music video recommendation is presented in this paper. The proposed method enables construction of the network that can accurately associate users with music videos corresponding to their preference by the collaborative use of audio and textual features obtained from music videos and social metadata “related videos”, “tags”, and “keywords” through sub-sampled canonical correlation analysis. By performing link prediction on the obtained network, our method enables users to obtain desired music videos that are not linked to each other in the network but corresponding to users' preference, that is, music video recommendation becomes feasible. Experimental results for real-world datasets show the effectiveness of our method.