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.
Highlights
As a cultural carrier, Chinese national music records the music and cultural life of people in different regions in different historical periods, specific cultural backgrounds, and different regions of the Chinese nation [1, 2]
If we look at this issue from the perspective of the development of Chinese folk music, the international communication of Chinese folk music has the positive significance of promoting China’s own development and enhancing the influence of Chinese folk music [16]. is article attempts to use the expectation-maximization algorithm to cluster users and ethnic music scores
The personalized music recommendation system has attracted the attention of many researchers, the development of personalized music recommendation still faces many challenges and problems, which need to be continuously studied and optimized
Summary
Chinese national music records the music and cultural life of people in different regions in different historical periods, specific cultural backgrounds, and different regions of the Chinese nation [1, 2]. In order to enhance the competitiveness of the industry, more and more music platforms use recommendation systems to provide users with quality services and personalized recommendation service. E collaborative filtering recommendation algorithm is faced with many problems in the application process; the most typical problems include cold start, data sparsity, and grey sheep user issues. To solve the grey sheep user problem and score sparsity problem in the music recommendation system, this paper proposes a coalition-based approach music recommendation algorithm with filter and playback coefficient. E playback coefficient of the user is calculated and used as the user attribute, and the cosine similarity formula is used to calculate user similarity; the user-based collaborative filtering algorithm is used to mine user preferences, to solve the grey sheep user and data sparsity problem User pairs are calculated through user playback information and frequency linear function singers’ ratings to solve the sparse user rating problem: first, the singer’s listening coefficient is calculated by the singer’s listening system. e playback coefficient of the user is calculated and used as the user attribute, and the cosine similarity formula is used to calculate user similarity; the user-based collaborative filtering algorithm is used to mine user preferences, to solve the grey sheep user and data sparsity problem
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