A Novel Knowledge Recommendation Method for Online Arts Education

Abstract

Recommendation algorithms are often used in shopping, movies, music and other fields because of their excellent performance in solving the information explosion problem. Although it has been around for a long time, its application in education has always been rare and lagging behind. In order to improve the applicability of the recommendation algorithm in the field of online arts education, and to solve the imperfect problem of professional knowledge system construction caused by the lack of paths in the learning process for arts professional knowledge learners, this paper proposes to combine the knowledge graph of the arts field with a novel knowledge graph recommendation method. An algorithm Co-RippleNet is introduced, which overcome RippleNet’s lack of explicit interest extraction by combining it with the co-word network. And through experiments compared with other recommended methods, it shows that this method has better performance on the education.