Research and Implementation of Music Recommendation System Based on Particle Swarm Algorithm

Music is an art form that expresses inner feelings, reflects social life and cultivates personal sentiment. With the network in digital form, which makes it increasingly their favorite works from the massive music data, resulting in a great demand for music recommendation systems. However, according to the previous research results, most of the existing music recommendation systems use static recommendation algorithms. When the data changes, the recommendation model needs to be rebuilt based on the entire data set. In large-scale data sets, static recommendation algorithms need to consume a lot of computing resources and time to reconstruct the recommendation model, and the efficiency is low. 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 the particle swarm algorithm has a higher degree of optimization for the music recommendation system and improves the of music selection, which the single system method.