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Bipartite Graph Link Prediction Method with Homogeneous Nodes Similarity for Music Recommendation by Lingling Zhang; Minghui Zhao; Daozhen Zhao is a Computer Science article available to read on EtoBox.

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Through analyzing users' listening records, personalized music recommendation can not only help users find interesting music, but also help related enterprises improve user loyalty. This paper proposes an improved music recommendation method based on bipartite graph link prediction with homogeneous nodes similarity. Firstly, users' music preference relations are expressed as positive and negative binary preference relations by the Complex Representationbased Link Prediction (CORLP) method, which improves the limitation of traditional recommendation method that can only represent unary preference relations. Secondly, the new method improves the CORLP method by attribute extraction and similarity calculation of homogeneous nodes including user nodes and music nodes. Thirdly, a new dataset based on the practical data from Shrimp Music Community is collected for facilitating the music recommendation task. The first-class music genres and second-class music genres of users are extracted by web crawling technology to calculate the similarity between user nodes. The rhythm and tempo are extracted by open source software to calculate the similarity between music nodes. Finally, the Top-N e

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Author

Lingling Zhang; Minghui Zhao; Daozhen Zhao

Publisher

Springer Science and Business Media LLC

Published

2020

Language

EN

Field

Computer Science (Physical Sciences)