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Personalized courses recommendation in Massive Open Online Courses (MOOCs) is the key to improve the learning experience. Traditional recommendation systems often overlook the diversity of user preferences and the consistency of users in different courses thereby limiting the accuracy and personalization of recommendations. To address these challenges, a Multi-View Attention Graph Convolutional Network (MVA-GCN) is proposed in this paper, which aims at extracting the complex relationships between courses and users more comprehensively to achieve precise prediction of user preferences. Firstly, a heterogeneous information network (HIN) from five types of entities and four types of relationships is constructed in the recommendation system. To mine the hidden relations of users in different meta-paths, we introduce a multi-view attention mechanism in MVA-GCN. And a contrastive learning based loss function is proposed to align entities in different meta-path views. Experimental results on the MOOC XuetangX dataset demonstrate that the MVA-GCN significantly outperforms recommendation baseline on multiple evaluation metrics, effectively validating the efficacy of the proposed method.
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