Abstract
With the rapid growth of the internet and online education resources, the number of massive open online courses (MOOCs) has increased dramatically, making it difficult for users to find personalized courses that meet their needs. Knowledge graphs (KGs) have been employed in recommendation systems to effectively address the issue of sparse interaction data in the MOOC scenarios for their rich semantic information. Research on KG-enhanced recommendation algorithms has found that the utilization of side information in KGs is crucial for improving accuracy. This paper introduces KGCN-UP (Knowledge Graph Convolutional Networks with User Preferences), a novel model for predicting the likelihood of a user interacting with a course based on user preferences and item relationships within a knowledge graph. The KGCN-UP model consists of two key modules. First, the user preference propagation module refines user preferences by exploring relational chains in the knowledge graph and dynamically adjusting attention to improve user representation. Second, the item neighbor enhancement module enhances item representations by aggregating semantic relationships and assigning attention weights based on the type of relationship between entities. Together, these components address the challenge of data sparsity and improve the quality of recommendations by leveraging high-order structural and semantic information. Empirical results on a real-world dataset demonstrate that KGCN-UP significantly outperforms existing state-of-the-art recommendation models in terms of accuracy.