R-CKGAT: a recommendation algorithm based on scientific fitness knowledge graph

Abstract

In recent years, with the spread and popularization of health knowledge, more and more people have begun to participate in fitness exercises to strengthen their bodies and prevent diseases. However, due to the lack of fitness knowledge base and the imperfection of fitness recommendation algorithm, fitness enthusiasts cannot obtain accurate fitness knowledge. Therefore, how to recommend personalized content for users according to their preferences has become a practical topic. Therefore, based on the knowledge graph technology, this paper constructs the scientific fitness knowledge graph, and proposes a model R-CKGAT that integrates collaborative knowledge embedding, user preference propagation and knowledge graph attention mechanism. Experimental results show that compared with MF, CKE and other baseline algorithms, the AUC and ACC values of the proposed algorithm in the scientific fitness data set are better than those baseline algorithms. The AUC and ACC of the model were 92.76% and 88.67% correspondingly.