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Hypergraph Convolutional Networks for Course Recommendation in MOOCs
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AI
DOI：10.1109/TKDE.2025.3568709.png)
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Abstract
En 中文
Mining learner preferences and needs from individual learning behavior data is a critical task in course recommendation systems. While graph-based models have shown efficacy in capturing pairwise relationships between learners and courses, they often overlook the complex higher-order interactions involving learners, courses and teachers that are essential for accurate recommendations. To address this limitation, we propose a novel Hypergraph Convolutional Network for Course Recommendation (HCNCR) framework, designed to model these higher-order interactions effectively. Our approach constructs course and learner hypergraphs based on course attributes and learner similarity relations, respectively. By employing hypergraph convolution, we capture the intrinsic higher-order relationships within these hypergraphs. Additionally, we utilize graph convolutional layers on the learner-course bipartite graph to integrate embeddings derived from hypergraphs, achieving comprehensive representations of both learners and courses. Extensive experiments conducted on real-world datasets demonstrate that HCNCR significantly outperforms existing state-of-the-art methods in course recommendation tasks.
Keywords:
Hypergraph convolution networks
higher-order relations
course recommendation
MOOCs
Journal
IF:
10.4
Papers:
6.8K
Citations:
3.2W