Abstract
The role and significance of recommendation system in music teaching is very important, but there is a problem of low management level. The recommendation system cannot solve the problem of processing multi-note data in music teaching, and the recommendation accuracy is poor. Therefore, this paper proposes particle swarm optimization to optimize the music recommendation system. Firstly, music teaching standards are used to classify music data, and selected according to the degree of compliance to realize the preprocessing of music data. Then, according to the degree of compliance, a systematic review collection is formed, and the evaluation results are analyzed. MATLAB simulation shows that the particle swarm algorithm has a higher degree of optimization for the music recommendation system and improves the compliance rate of music selection, which is better than the single system method.
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Chen, Y. (2024). Research and Implementation of Music Recommendation System Based on Particle Swarm Algorithm. In: Hung, J.C., Yen, N., Chang, JW. (eds) Frontier Computing on Industrial Applications Volume 1. FC 2023. Lecture Notes in Electrical Engineering, vol 1131. Springer, Singapore. https://doi.org/10.1007/978-981-99-9299-7_18
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DOI: https://doi.org/10.1007/978-981-99-9299-7_18
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