Title: Personalised recommendation algorithm of music resources based on category similarity

Authors: Linqian Peng; Da Li

Addresses: Academy of Music and Dance, Hunan City University, Yiyang 413000, Hunan, China ' Academy of Music, Jianghan University, Wuhan 430056, Hubei, China

Abstract: Because the collaborative filtering algorithm cannot achieve accurate matching, a personalised music resource recommendation algorithm based on category similarity is proposed. The user preference type parameters are obtained by the modelling and analysis method of knowledge graph and the personalised preference judgement model is established according to the type parameters. The feature registration algorithm is used to mine the personalised features of music resources, and the homomorphic reliability of the personalised scores is analysed to build a joint parameter matching model of music resources. Finally, through the analysis of category similarity characteristics, the adaptive statistical analysis of music resources is carried out, and the personalised feature parameters of music resources are extracted to achieve personalised recommendation of music resources. The simulation results show that the minimum satisfaction of the method in the optimal state is 92.7%, the resource holding level is always above 92%, and the recommended accuracy is 98.9%, which shows that the method in this paper is more practical.

Keywords: category similarity; music resources; personalised; recommendation; preference; contribution level.

DOI: 10.1504/IJRIS.2023.136369

International Journal of Reasoning-based Intelligent Systems, 2023 Vol.15 No.3/4, pp.323 - 331

Received: 11 Jul 2022
Accepted: 22 Nov 2022
Published online: 31 Jan 2024 *

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