Title: A personalised recommendation method of pop music based on machine learning

Authors: Honghao Yu

Addresses: School of Music, Hunan City University, Yiyang 413000, China

Abstract: In order to enhance the satisfaction of pop music personalised recommendation and improve the accuracy and efficiency of pop music personalised recommendation, a pop music personalised recommendation method based on machine learning is proposed. Firstly, the relevant theories of machine learning and short-term and long-term memory artificial neural networks are studied, and then the popular music word vector is extracted by using softmax function, and the collaborative filtering algorithm with weighting factor is introduced to calculate the similarity of popular music word vector. Finally, based on the LSTM network, a pop music personalised recommendation model is constructed to realise pop music personalised recommendation. Experiments show that the method proposed in this paper has a personalised recommendation satisfaction index of 97.8% for pop music, the recommendation time is only 19.4s, and the average value of MAPE and RMSE are only 0.037 and 0.039 respectively. The recommendation accuracy, satisfaction and efficiency are high, and the design purpose can be achieved.

Keywords: machine learning; Word2Vec; LSTM network; popular music; personalised recommendation.

DOI: 10.1504/IJRIS.2023.130211

International Journal of Reasoning-based Intelligent Systems, 2023 Vol.15 No.2, pp.120 - 127

Received: 16 May 2022
Accepted: 14 Jul 2022
Published online: 06 Apr 2023 *

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