Research on Personalized Course Resource Recommendation Method Based on GEMRec

作     者：Wang, Enliang Sun, Zhixin 

作者机构：Nanjing Univ Posts & Telecommun Post Big Data Technol & Applicat Engn Res Ctr Jian Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Post Ind Technol Res & Dev Ctr State Posts Bur Int Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Key Lab Broadband Wireless Commun & Sensor Network Minist Educ Nanjing 210003 Peoples R China 

出 版 物：《APPLIED SCIENCES-BASEL》 (Appl. Sci.)

年 卷 期：2025年第15卷第3期

页      面：1075-1075页

核心收录：

基　　金：National Natural Science Foundation of China Jiangsu Agriculture Science and Technology Innovation Fund (JASTIF) [CX(22)1007] 61972208 62272239 

主　　题：multimodal deep learning knowledge graph personalized course recommendation graph neural network interpretability 

摘      要：With the rapid growth of online educational resources, existing personalized course recommendation systems face challenges in multimodal feature integration and limited recommendation interpretability when dealing with complex and diverse instructional content. This paper proposes a graph-enhanced multimodal recommendation method (GEMRec), which effectively integrates text, video, and audio features through a graph attention network and differentiable pooling. Innovatively, GEMRec introduces graph edit distance into the recommendation system to measure the structural similarity between a learner s knowledge state and course content at the knowledge graph level. Additionally, it combines SHAP (SHapley Additive exPlanations) value computation with large language models to generate reliable and personalized recommendation explanations. Experiments on the MOOCCubeX dataset demonstrate that the GEMRec model exhibits strong convergence and generalization during training. Compared with existing methods, GEMRec achieves 0.267, 0.265, and 0.297 on the Precision@10, Recall@10, and NDCG@10 metrics, respectively, significantly outperforming traditional collaborative filtering and other deep learning models. These results validate the effectiveness of multimodal feature integration and knowledge graph enhancement in improving recommendation performance.