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

The research and implementation of recommendation system is critical in music recommendation system; however, it has an issue with erroneous performance positioning. The typical ant colony algorithm is unable to address the particle swarm algorithm issue in music recommendation system, and the result is insufficient. As a result, a particle swarm arithmetic-based music recommendation system research and implementation of recommendation system is provided, and it is assessed. To begin, the swarm intelligence theory is used to discover the influencing elements, and the indicators are split based on the research and implementation of recommendation system's needs to decrease interference factors in the research and implementation of recommendation system. The swarm intelligence theory is then used to create a particle swarm arithmetic research and implementation of recommendation system scheme, and the outcomes of the research and implementation of recommendation system are thoroughly examined. The MATLAB simulation results reveal that, under evaluation conditions, the particle swarm arithmetic outperforms the standard ant colony algorithm in terms of research and implementation of recommendation system accuracy and time of influencing variables.