Hybrid Music Recommendation Algorithm Based on Music Gene and Improved Knowledge Graph

Abstract

Combining music as a specific recommendation object, a hybrid recommendation algorithm based on music genes and improved knowledge graph is proposed for the traditional single recommendation algorithm that cannot effectively solve the accuracy problem in music recommendation. The algorithm first gives the recommendation pattern of music genes and gets the relevant recommendation results through the genetic preference analysis. After that, the algorithm in this paper utilizes item and user label information and knowledge graphs from two different domains to enrich and mine the potential information of users and items. In addition, deep learning method is applied to extract low-dimensional, abstract deep semantic features of users and items, based on which, score prediction is performed. The mixed-mode based recommendation addresses the drawbacks of these two recommendations and can adopt different weighting strategies in different situations. The advantages of music gene and knowledge graph-based recommendation algorithms are combined via this method. The experimental results indicate that the algorithm in this paper outperforms other existing recommendation algorithms.