Personalized Music Recommendation Algorithm Based on Hybrid Collaborative Filtering Technology

Abstract

With the continuous growth of music resources, the problem of recommending suitable music for users has become a research hotspot. In this paper, association rules and music genes are added to music collaborative filtering personalized recommendation system to establish a hybrid recommendation model. The structure of the model is described and the recommendation process and recommendation algorithm of personalized recommendation are described in detail. By analyzing users' interests and preferences for different music gene features, the algorithm comprehensively analyses users' behavior, and uses the similarity of interests among different users to construct the neighborhood relationship among them. The recommendation algorithm is validated by combining two factors, and the expected recommendation results are achieved.