Application of Collaborative Filtering and Data Mining Technology in Personalized National Music Recommendation and Teaching

Abstract

Personalized music recommendations can accurately push the music of interest from a massive song library based on user information when the user’s listening needs are blurred. To this end, this paper proposes a method of national music recommendation based on ontology modeling and context awareness to explore the use of music resources to portray user preferences better. First, the expectation-maximization algorithm is used to cluster users and ethnic music scores, and similar users and music are divided into clusters. The similarity of objects in the same cluster is higher, and the similarity of objects in different clusters is lower. Second, we designed a multilayer collaborative filtering ethnic music recommendation model based on ontology modeling and tensor decomposition. This model uses ontology to construct a user knowledge model and integrates similarity measures in multiple situations. The actual case test and user feedback analysis show that the designed personalized national music model has good application and promotion effects.