Music Video Recommendation Based on Link Prediction Considering Local and Global Structures of a Network

IEEE Access

Ryosuke Harakawa¹  Miki Haseyama²  Takahiro Ogawa²  Yui Matsumoto³ 

[1] Department of Electrical, Electronics and Information Engineering, Nagaoka University of Technology, Nagaoka, Japan;Faculty of Information Science and Technology, Hokkaido University, Sapporo, Japan;Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan;

关键词: Music video;    recommendation;    link prediction;    network analysis;    social metadata;   

DOI  :  10.1109/ACCESS.2019.2930713

来源: DOAJ

【 摘 要 】

A novel method for music video recommendation is presented in this paper. The contributions of this paper are two-fold. (i) The proposed method constructs a network, which not only represents relationships between music videos and users but also captures multi-modal features of music videos. This enables collaborative use of multi-modal features such as audio, visual, and textual features, and multiple social metadata that can represent relationships between music videos and users on video hosting services. (ii) A novel scheme for link prediction considering local and global structures of the network (LP-LGSN) is newly derived by fusing multiple link prediction scores based on both local and global structures. By using the LP-LGSN to predict the degrees to which users desire music videos, the proposed method can recommend users' desired music videos. The experimental results for a real-world dataset constructed from YouTube-8M show the effectiveness of the proposed method.

【 授权许可】

Unknown