# Research on Personalized Course Resource Recommendation Method Based on GEMRec
## 4 Experiments and Results Analysis
### Knowledge Graph Similarity Calculation Experiment  
This section provides a detailed analysis of the experimental results of the recommendation model based on graph edit distance within the course knowledge graph system. Our experiments cover aspects such as knowledge graph storage structure, cache performance, graph edit distance approximation accuracy, node embedding effectiveness, course similarity analysis, model training process, and a final comparison of recommendation accuracy. Graph edit distance serves as a core component of our recommendation model. Figure 9 presents the accuracy analysis results of the graph edit distance approximation algorithm. Figure 8: Recommendation system entity relationship extraction results (partially visualized). Figure 9: Analysis of approximate accuracy of graph editing distance. Figure 10 shows the approximation accuracy of the graph edit distance. The scatter plot indicates that most points are distributed near the ideal fitting line, demonstrating that our approximation algorithm can accurately estimate the actual graph edit distance. The calculated correlation coefficient is 0.9569, suggesting that using an efficient approximation method instead of an exact but time-consuming graph edit distance calculation can improve system response speed while maintaining accuracy. Figure 9 presents the similarity matrix between different courses in the form of a heat map. The changes in the loss function and validation accuracy during model training are shown in Figure 11.  
Figure 11: Training process log. Figure 10: Course similarity heatmap. The curves for training and validation loss and accuracy indicate good model performance. As training progresses, the loss decreases from 0.6 to approximately 0.1, and accuracy increases from 0.4 to nearly 1.0, with validation accuracy reaching 0.9, indicating good generalization capability. We compared the performance of different recommendation methods using Precision@10, Recall@10, and NDCG@10 as metrics. Precision@10 measures the accuracy of recommendations, Recall@10 reflects the system's coverage of user preferences, and NDCG@10 evaluates the ranking quality of recommendations, with higher values indicating more accurate, comprehensive, and well-ordered recommendations. As shown in Table 1, our method achieved a Precision@10 of 0.267, significantly outperforming various recommendation algorithms. This result demonstrates that the feature representation enhanced by multimodal fusion and knowledge graphs enables more accurate recommendations. For Recall@10, our model reached 0.265, exceeding most comparison methods except CAmgr, which also leverages multimodal information fusion and graph structure processing. Our model achieved an NDCG@10 of 0.297, surpassing other algorithms, including CAmgr, which validates that our unique combination of cross-attention mechanism and knowledge graph path inference optimizes recommendation ranking more effectively. While CAmgr demonstrates strong performance due to its similar technical approach of incorporating multimodal fusion and graph processing, our method's superior results can be attributed to the innovative integration of graph edit distance and differentiable pooling. Traditional models and earlier deep learning approaches performed less effectively, mainly due to their limited ability to process multimodal information and leverage knowledge graph structures. Notably, in cold-start situations (with fewer than five user interactions), our method performed even better, as shown in Figure 12.  
As shown in Figure 12, we specifically evaluated the algorithms' performance under cold-start conditions, where users have fewer than five interactions with the system. This scenario is particularly challenging yet crucial in educational recommendations, as new learners need accurate course suggestions before accumulating sufficient interaction history. Our GEMRec method achieved an NDCG@10 of 0.372 in these cold-start scenarios, outperforming both KGCN (0.348) and CAmgr (0.364).