Personalized Music Recommendation Based on Clustering Algorithm

Abstract

Aiming at the recommendation of music field, this paper proposes a music recommendation algorithm based on attribute selection and application clustering. Firstly, the recommended progress of music conduct in-depth analysis to focus on building properties of music and the problems of interaction in the field of music recommendation. With clustering algorithm as the main method, more accurate clustering will make the recommendation more precise. Based on attribute building and clustering, the overall recommendation scheme is designed, and the music is clustered by attribute judgment. Experimental results show that music recommendation proposed algorithm has a better recommendation effect, can effectively improve the user experience.