AI-Based Music Recommendation Algorithm under Heterogeneous Network Platform

Abstract

Music service is one of the diversified network services offered by people in the Internet era. Various music websites provide many tracks to meet people's music needs. Hundreds of millions of music of various genres at home and abroad, and there is a severe problem of information asymmetry between users and music. As a branch of the information filtering system, the recommendation system can predict users' preferences, increase flow, and drive consumption. A personalized music recommendation system can effectively provide people with a list of favorite tracks. Recently, many researchers have paid attention to heterogeneous networks because of their rich semantics information. Research has confirmed that rich relationship information in heterogeneous networks can improve the recommendation effect. Therefore, under the platform of a heterogeneous network, this paper divides the digraph set of track characteristics into several clusters with maximum heterogeneity, which makes the digraph of track characteristics in each cluster isomorphic to the maximum extent. When matching similarity, only searching in the cluster with the highest similarity to the target user can match a sufficient amount of applicable tracks, thus improving the efficiency of music recommendations to users. Experimental results show that the proposed algorithm has a high recall, precision, and F1 and can recommend personalized track lists to users to meet their music needs.