Enhancing Course Recommendation with LLM-Generated Concepts: A Unified Framework for Side Information Integration

Massive Open Online Courses (MOOCs) have gained increasing popularity in recent years, highlighting the growing importance of effective course recommendation systems (CRS). However, the performance of existing CRS methods is often limited by data sparsity and suffers under cold-start scenarios. One promising solution is to leverage course-level conceptual information as side information to enhance recommendation performance. We propose a general framework for integrating LLM-generated concepts as side information into various classic recommendation algorithms. Our framework supports multiple integration strategies and is evaluated on two real-world MOOC datasets, with particular focus on the cold-start setting. The results show that incorporating LLM-generated concepts consistently improves recommendation quality across diverse models and datasets, demonstrating that automatically generated semantic information can serve as an effective, reusable, and scalable source of side knowledge for educational recommendations. This finding suggests that LLMs can function not merely as content generators but as practical data augmenters, offering a new direction for enhancing robustness and generalizability in course recommendation.