Construction of personalized learning service system based on deep learning and knowledge graph

Abstract

Abstract With the development of information technology, it is difficult for people to find effective information from the massive Internet information, and personalized services are in short supply. This paper explores the essential association between knowledge graphs and deep learning and examines the construction process of knowledge graphs. Based on the content and collaborative filtering recommendation method, the theoretical method of personalized learning service recommendation is being studied. Knowledge graphs are introduced in collaborative filtering to accurately portray user and item vector representations. The knowledge graph is used to represent the TransR model, forming an entity space and a multivariate relationship space. The students’ demand for personalized learning service systems is analyzed from the user’s point of view, the personalized learning service system is constructed, and the accuracy of the personalized learning service system constructed in this paper is analyzed. The accuracy rate of DLKG is 0.1425 when K=10, which is the highest among the five models. In the effect of personalized teaching, the mean value of the metacognitive ability of the students in the experimental class is higher than that of the control class by 0.4155, 0.0261, 0.3995, 0.7967, respectively, and the experiment proves that the personalized learning service system based on the DLKG model proposed in this paper is more effective.