H-BERT4Rec: Enhancing Sequential Recommendation System on MOOCs Based on Heterogeneous Information Networks

Massive Open Online Courses (MOOCs) have become a pioneer in providing access to knowledge for everyone around the world. These courses transcend geographical and linguistic barriers, allowing anyone, anywhere, to learn and enhance their knowledge. Although previous research on recommendation models has shown promising results in course recommendations, building such systems for MOOC platforms still presents significant challenges. Implicit user feedback often lacks explicit negative signals, making it difficult to accurately model user preferences. Additionally, the diversity and sparsity of data, especially for new or niche courses, further hinder traditional methods. This creates a pressing need for new and improved solutions in this field. In this study, we propose H-BERT4Rec, an enhancement of the BERT4Rec model, which leverages Heterogeneous Information Networks (HINs) to address these challenges. HINs integrate diverse data sources and capture complex relationships between entities such as courses, videos, and users. This not only enhances the understanding of user preferences but also strengthens the ability to recommend suitable courses. H-BERT4Rec utilizes Heterogeneous Network Embedding(HNE)-node embedding generation method that leverages HIN to create Pre-train Embeddings, and then improves the BERT4Rec architecture, leading to more accurate and personalized recommendations. We conduct experiments on a real-world MOOC dataset to demonstrate the superior performance of H-BERT4Rec compared to baseline models, achieving an improvement of up to 55,04%. This study contributes a promising new approach for personalized course recommendations in MOOCs, enhancing the learning experience for millions of learners worldwide. This improvement not only promises significant benefits for learners but also opens up new directions for research and development in the field of online education.