Hua Zhong, Yang Jianbai, Ji Weidong
College of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, China.
Sci Rep. 2025 Aug 18;15(1):30256. doi: 10.1038/s41598-025-14150-5.
With the rapid growth of the internet and online education resources, the number of massive open online courses (MOOCs) has increased dramatically, making it difficult for users to find personalized courses that meet their needs. Knowledge graphs (KGs) have been employed in recommendation systems to effectively address the issue of sparse interaction data in the MOOC scenarios for their rich semantic information. Research on KG-enhanced recommendation algorithms has found that the utilization of side information in KGs is crucial for improving accuracy. This paper introduces KGCN-UP (Knowledge Graph Convolutional Networks with User Preferences), a novel model for predicting the likelihood of a user interacting with a course based on user preferences and item relationships within a knowledge graph. The KGCN-UP model consists of two key modules. First, the user preference propagation module refines user preferences by exploring relational chains in the knowledge graph and dynamically adjusting attention to improve user representation. Second, the item neighbor enhancement module enhances item representations by aggregating semantic relationships and assigning attention weights based on the type of relationship between entities. Together, these components address the challenge of data sparsity and improve the quality of recommendations by leveraging high-order structural and semantic information. Empirical results on a real-world dataset demonstrate that KGCN-UP significantly outperforms existing state-of-the-art recommendation models in terms of accuracy.
随着互联网和在线教育资源的快速增长，大规模开放在线课程（MOOC）的数量急剧增加，这使得用户难以找到符合其需求的个性化课程。知识图谱（KG）已被应用于推荐系统中，因其丰富的语义信息，能够有效解决MOOC场景中交互数据稀疏的问题。对基于知识图谱增强的推荐算法的研究发现，利用知识图谱中的辅助信息对于提高准确性至关重要。本文介绍了KGCN-UP（带用户偏好的知识图谱卷积网络），这是一种基于知识图谱中的用户偏好和项目关系来预测用户与课程交互可能性的新型模型。KGCN-UP模型由两个关键模块组成。首先，用户偏好传播模块通过探索知识图谱中的关系链并动态调整注意力来细化用户偏好，从而改善用户表示。其次，项目邻居增强模块通过聚合语义关系并根据实体之间关系的类型分配注意力权重来增强项目表示。这些组件共同应对数据稀疏的挑战，并通过利用高阶结构和语义信息提高推荐质量。在真实数据集上的实证结果表明，KGCN-UP在准确性方面显著优于现有的最先进推荐模型。
