Development of Personalized Learning Resources Recommendation System Based on Knowledge Graph

Abstract

This study focuses on how to efficiently and accurately recommend personalized learning resources for users when they are in the face of massive learning resources. A recommendation system is developed with software engineering methods. Knowledge graph technology is integrated in the system; curriculum knowledge graphs are constructed to solve the problems of semi-structured data storage and knowledge fragmentation. Two kinds of recommendation methods are adopted to achieve the goal of learners' personalized learning, one is Euclidean distance recommendation algorithm based on user behavior graph library, the other is learning mode recommendation algorithm and sequential mode recommendation algorithm based on user session library. The recommendation system maintains the interpret-ability and the accuracy of recommendation based on user historical behavior data; and realizes recommendation based on user session library in the context of lacking users’ historical data.