EduGraph: Learning Path-Based Hypergraph Neural Networks for MOOC Course Recommendation

In online learning, personalized course recommendations that align with learners’ preferences and future needs are essential. Thus, the development of efficient recommender systems is crucial to guide learners to appropriate courses. Graph learning in recommender systems has been extensively studied, yet many models focus on low-frequency information, underscoring similar learner preferences and overlooking high-frequency data that indicates varied learning trajectories. Furthermore, course co-occurrence and sequential relationships are often insufficiently investigated. In this paper, we introduce <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>EduGraph</b></monospace>, a novel framework developed specifically for MOOC course recommendation systems. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>EduGraph</b></monospace> is characterized by its incorporation of a learning path-based hypergraph, a unique perspective wherein learners are represented as hyperedges, and courses are delineated as vertices. The framework incorporates a framelet-based hypergraph convolution, integrating low-pass filters to highlight similarities and high-pass filters to underscore distinct learning paths among learners. Furthermore, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>EduGraph</b></monospace> features a dual hypergraph learning model, with channels designated for vertex and hyperedge encoding, fostering a collaborative information exchange that refines the learners’ preference embeddings. The empirical assessment of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>EduGraph</b></monospace> is conducted through a comprehensive comparison with many existing baselines, utilizing two distinct MOOC datasets. Our experimental studies not only emphasize the enhanced recommendation performance of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>EduGraph</b></monospace> but also elucidate the significant contributions of its individual components, such as the integration of low-pass and high-pass filters and the framelet-wise collaborative strategy that effectively bridges hyperedge-level and vertex-level representations, augmenting the overall efficacy of the course recommendation system.