# Research on Personalized Course Resource Recommendation Method Based on GEMRec
###### Abstract  
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.  
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Footnote †: journal: Applied sciences  
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the ability to capture complex nonlinear interactions through deep feature extraction. However, single-modality recommendation systems struggle to effectively integrate multimodal data, such as text, video, and audio, resulting in inadequate cross-modal information fusion and user interest understanding. Multimodal deep learning, through the integration of various feature types, provides a more comprehensive understanding of course content, thereby enhancing recommendation effectiveness. Nonetheless, existing multimodal recommendation systems still face challenges in modality feature fusion and weight allocation, and they lack interpretability, which limits system transparency and user trust.  
The application of knowledge graphs in recommendation systems offers unique advantages, as they enable structured pathways that link users to learning resources, aiding in understanding the knowledge structure of courses. However, integrating knowledge graphs with deep learning to enhance a system's ability to understand course content, capture knowledge relationships, and provide interpretable recommendations remains a pressing challenge.  
To address these challenges, this paper proposes a multimodal deep learning recommendation framework, GEMRec, enhanced by heterogeneous knowledge graphs. This approach includes the following theoretical innovations: (1) A cross-modal feature fusion mechanism based on a graph attention network, which employs differentiable pooling and multi-head attention to achieve hierarchical representation learning and dynamic weight allocation for heterogeneous data such as text, video, and audio. (2) A bidirectional enhancement learning paradigm that combines knowledge graphs and graph neural networks, leveraging graph edit distance-based similarity calculations and meta-path-based sequential representations to capture the intrinsic associations within course knowledge structures. (3) A graph-semantic-enhanced interpretability framework based on SHAP (SHapley Additive exPlanations) values and large language models, which quantifies feature importance and generates dynamic templates to provide interpretable recommendations. Experimental results demonstrate significant improvements in recommendation accuracy, knowledge representation, and system transparency. This research offers a novel theoretical paradigm and technical approach to addressing the challenges of multimodal feature fusion, knowledge representation learning, and interpretability in educational recommendation systems.