DRG: A dual relational graph framework for course recommendation

The course recommendation system is a core application of recommendation technology in the educational field. Its significance lies in accurately matching users' interests and needs while providing valuable feedback to instructors, thereby fostering continuous improvement in teaching quality. Various techniques have been proposed for this purpose, with Large Language Models (LLMs) demonstrating significant potential in course recommendation tasks. However, the issue of data sparsity remains a critical bottleneck that limits the accuracy of the recommendation. In this study, we propose a Dual Relationship Graph (DRG) framework that addresses data sparsity by modeling both course-course and user-course relationships through a dual-graph structure. Specifically, DRG constructs two relational graphs: a course-based graph built using LLM-based semantic reasoning, collaborative filtering, clustering, and association rule mining; and a user-based graph constructed via collaborative filtering and LLM-based preference inference. These graphs are integrated into a unified recommendation pipeline through joint graph learning and collaborative reasoning. The enhanced interaction graphs significantly alleviated sparsity, increasing link coverage by 37.88 % and 12.67 % on the two datasets, respectively. Notably, DRG is designed as a plug-and-play module, compatible with both traditional models and LLM-based recommendation systems. Experimental results show that our DRG excels in task ranking across two benchmark datasets, significantly enhancing traditional recommendation models and LLM-based methods. Moreover, DRG's dual relationship graph consistently outperforms single relationship approaches, underscoring the importance of multi-perspective integration in course recommendation systems. By unifying dual-perspective graph modeling with LLM-driven semantic understanding, DRG provides a scalable and effective solution for personalized course recommendation in sparse educational environments. The code and datasets will be made available at https://github.com/WHCK1102/DRG.