Personalised recommendation algorithm of music resources based on category similarity

被引：0

作者：

Peng L. ^{[1
]}

Li D. ^{[2
]}

机构：

[1] Academy of Music and Dance, Hunan City University, Yiyang, Hunan

[2] Academy of Music, Jianghan University, Wuhan, Hubei

来源：

International Journal of Reasoning-based Intelligent Systems | 2023年 / 15卷 / 3-4期

关键词：

Category similarity; Contribution level; Music resources; Personalised; Preference; Recommendation;

D O I：

10.1504/IJRIS.2023.136369

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. © 2023 Inderscience Publishers. All rights reserved.

引用

页码：323 / 331

页数：8

共 20 条

[11]

Meng X.W., Liu S.D., Zhang Y.J., Et al., Research on social recommender systems, Journal of Software, 26, 6, pp. 1356-1372, (2015)

[12]

Sodhro A.H., Obaidat M.S., Abbasi Q.H., Et al., Quality of service optimization in an IoT-driven intelligent transportation system, IEEE Wireless Communications, 26, 6, pp. 10-17, (2019)

[13]

Tim N., Zorita E., Honicke B., Decadal variability and trends of the Benguela upwelling system as simulated in a high-resolution ocean simulation, Ocean Science, 11, 3, pp. 483-502, (2015)

[14]

Tu D.D., Shu C.C., Yu H.Y., Using unified probabilistic matrix factorization for contextual advertisement recommendation, Journal of Software, 24, 3, pp. 454-464, (2013)

[15]

Wang Y., Zhu J., Wang Z., Bai F., Gong J., Review of applications of natural language processing in text sentiment analysis, Journal of Computer Applications, 42, 4, pp. 1011-1020, (2022)

[16]

Yu L.J., Research on personalized music recommendation algorithm based on deep learning, Microcomputer Applications, 36, 10, pp. 140-143, (2020)

[17]

Yuan Z., Chen R., Collaborative filtering recommendation algorithm based on multi-level hybrid similarity, Journal of Computer Applications, 38, 3, pp. 633-638, (2018)

[18]

Zhang F.Z., Liu S., Li Z.H., Et al., Collaborative filtering recommendation algorithm incorporating user's reviews and contextual information, Journal of Chinese Computer Systems, 35, 2, pp. 228-232, (2014)

[19]

Zhang M., Liu D.X., Research and implementation of music recommendation system based on collaborative filtering algorithm, Electronic World, 10, 4, pp. 63-64, (2020)

[20]

Zhang X., Wang C., Lyu Y., Lin Y., Hierarchical classification online streaming feature selection algorithm based on ReliefF algorithm, Journal of Computer Applications, 42, 3, pp. 688-694, (2022)

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