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

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 en...

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Bibliographic Details
Published inIEEE access Vol. 7; pp. 104155 - 104167
Main Authors Matsumoto, Yui, Harakawa, Ryosuke, Ogawa, Takahiro, Haseyama, Miki
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.01.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Feature extraction
History
Information science
link prediction
Metadata
Music
Music video
Music videos
network analysis
recommendation
Recommender systems
social metadata
Video
Visualization
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2930713