Course Recommendation Method Based on Dual-End Collaborative Information of Knowledge Graph

Yizhi Zhang

¹School of Artificial Intelligence and Computer Science, Shaanxi Normal University, Xi’an, Shaanxi, 710062, China

²School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang, Henan 453003, China

*Corresponding author

Knowledge graphs contain rich semantic information, and their integration into recommendation systems has significantly alleviated challenges such as data sparsity and the cold-start problem. However, existing knowledge graph-based recommendation methods primarily focus on optimizing the item side of the graph, often neglecting the user side. This limitation leads to insufficient utilization of explicit collaborative information derived from user–item interactions, resulting in embedding representations that fail to effectively capture the latent semantics of both users and items. To address this issue, we propose a novel approach, Course Recommendation Method Based on Dual-End Collaborative Information of Knowledge Graph (DCIKG-Rec). The proposed method simultaneously models both users and items, enabling the integration of collaborative information and knowledge associations through heterogeneous propagation techniques to enhance representation learning. Furthermore, DCIKG-Rec employs a knowledge-aware attention mechanism to evaluate the importance of neighbors at each layer for different entities, and a bias-based attention mechanism to preserve collaborative information during multi-layer propagation. Finally, the learned representations of users and items are utilized to predict the probability of user–item interactions. Extensive experiments conducted on a real-world dataset demonstrate that DCIKG-Rec achieves an AUC of 0.8964 and an F1 score of 0.7952 in click-through rate prediction. In addition, its Top-K recommendation performance shows superior recall compared with several state-of-the-art baseline models.

Recommendation System, Collaborative Filtering, Heterogeneous Propagation, Knowledge Graph

Yizhi Zhang, Xinyuan Ji, Danni Yan, Liming Xu, Bello Ahmad Muhammad (2025). Course Recommendation Method Based on Dual-End Collaborative Information of Knowledge Graph. Journal of Networking and Network Applications, Volume 5, Issue 4, pp. 148–157. https://doi.org/10.33969/J-NaNA.2025.050401.

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