Enhancing MOOCs Personalized Recommendation with Graph Neural Networks and Attention Mechanisms

Abstract

Abstract Massive open online courses (MOOCs) have revolutionized education, providing unprecedented access to knowledge and skills to learners worldwide. While traditional methods have achieved comparable performance in personalized recommendations, they suffer from two key limitations. Firstly, they fail to capture the rich relationships between courses and users embedded within the MOOC graph structure. Secondly, they disregard the sequential nature of user learning, neglecting the evolving preferences and interests over time. These methods often overlook the recency of items, potentially neglecting relevant and trending courses. This paper presents a personalized recommendation approach for MOOCs that combines the effectiveness of an Attention mechanism with the capabilities of a Graph Neural Network, namely AGNN, to tackle this problem. This novel recommendation system in MOOCs leverages GNNs for rich learner-course relationships and LSTM for dynamic user preferences, culminating in personalized recommendations through MF-BPR learning. Real-world course data experiments demonstrate AGNN’s ability to significantly improve recommendation performance. An in-depth ablation study further underscores the critical influence of attention mechanisms, highlighting the model’s ability to dynamically adapt to evolving user preferences and prioritize recent, relevant items, ultimately leading to more personalized and effective recommendations.