Journal Article

A Learning Resource Recommendation Method Based on Graph Contrastive Learning

Wei, Jianguo, Lu, Wenhuan, Dang, Jianwu, Yong, Jiu, Lei, Xiaomei, Cheng, Meijuan

2025

Overview

The existing learning resource recommendation systems suffer from data sparsity and missing data labels, leading to the insufficient mining of the correlation between users and courses. To address these issues, we propose a learning resource recommendation method based on graph contrastive learning, which uses graph contrastive learning to construct an auxiliary recommendation task combined with a main recommendation task, achieving the joint recommendation of learning resources. Firstly, the interaction bipartite graph between the user and the course is input into a lightweight graph convolutional network, and the embedded representation of each node in the graph is obtained after compilation. Then, for the input user–course interaction bipartite graph, noise vectors are randomly added to each node in the embedding space to perturb the embedding of graph encoder node, forming a perturbation embedding representation of the node to enhance the data. Subsequently, the graph contrastive learning method is used to construct auxiliary recommendation tasks. Finally, the main task of recommendation supervision and the constructed auxiliary task of graph contrastive learning are jointly learned to alleviate data sparsity. The experimental results show that the proposed method in this paper has improved the Recall@5 by 5.7% and 11.2% and the NDCG@5 by 0.1% and 6.4%, respectively, on the MOOCCube and Amazon-Book datasets compared with the node enhancement methods. Therefore, the proposed method can significantly improve the mining level of users and courses by using a graph comparison method in the auxiliary recommendation task and has better noise immunity and robustness.

• Publisher: MDPI AG
• Subject: Algorithms / Artificial neural networks / Collaboration / Computational linguistics / Data mining / Distance learning / Embedding / Equipment and supplies / Graph comparison / Graph representations / Graph theory / Graphical representations / Information overload / Language processing / Learning / Missing data / Natural language interfaces / Neural networks / Nodes / Online instruction / Recommender systems / Sparsity / Teaching