EGRec: A MOOCs Course Recommendation Model Based on Knowledge Graphs

Massive Open Online Courses (MOOCs) provide abundant learning resources but also overwhelm learners with their sheer volume, leading to challenges such as data sparsity and cold-start issues in conventional recommendation systems. To address these challenges, we propose EGRec, a novel course recommendation model that combines knowledge graphs and Heterogeneous Graph Attention Networks to improve recommendation precision, diversity, and relevance. By integrating multimodal data, EGRec captures intricate semantic relationships between courses and knowledge points, enabling personalized and context-sensitive recommendations. Extensive experiments on real MOOCs datasets demonstrate that EGRec significantly outperforms traditional models, highlighting its potential to enhance tailored learning experiences.