Graph Neural Network Integrating Hot Spots and Long and Short-Term Interests for Course Recommendation

Abstract

In recent years, massive online open courses (MOOCs) platforms provide users with a wealth of learning resources. Nevertheless, information overload remains a pressing concern, necessitating the development of effective personalized course recommendation methods. The existing course recommendation methods disregard the temporal relationship among courses and are unable to capture long-distance dependencies between them. Simultaneously, personalized course recommendation models designed for interactive sequence modeling are confronted with two key issues: how to extract users’ learning interest representation effectively and how to solve cold-start. Based on this, a graph neural network course recommendation model (GHLS4CR) is proposed, which integrates hot spots and long and short-term interests. This model designs two session graph conversion methods, acyclic timing graph and acyclic shortcut graph, to alleviate the problems of temporal information loss and inability to capture long-distance dependencies in existing methods. This model represents users’ long-term and short-term interests at the graph level, and integrates them with popular course information to achieve personalized recommendations while alleviating cold-start issue. A large number of experiments conducted on the XuetangX public dataset MOOCCourse show that GHLS4CR outperforms mainstream recommendation models such as FISSA and LESSR in the field of personalized course recommendation. Compared with the second best LESSR model, Recall@5 is improved by 13.28%, and MRR@5 is improved by 15.50%.

Keywords

• course recommendation; session-based recommendation; graph neural networks; long and short-term interests; cold-start