# Enhancing the Recommendation of Learning Resources for Learners via an Advanced Knowledge Graph
## 3 Materials and Methods
### Performance Comparison  
Table 2 presents the AUC and F1 values for each model. Through performance comparison analysis, we can see that our method achieved the best performance on the Book-Crossing dataset in terms of the evaluated metrics.  
* The path-based methods performed the worst in experiments on the Book-Crossing dataset, even worse than the classic collaborative filtering method, BPRMF, indicating that the experimental results of path-based methods were heavily dependent on the choice of paths.
* The propagation-based method combines the semantic representations of entities and relationships with path connectivity, and its effectiveness is superior to the first two methods. Experimental data showed that RippleNet improved the AUC metric by 10.1% compared to PER.
* KGAT captures the differential importance of different neighbor nodes during the propagation process through an attention mechanism, which positively contributes to improving the performance of knowledge graph representation learning models. Its results showed an improvement of 0.6% and 0.4% in the AUC and F1 metrics, respectively, compared to RippleNet. This indicates that the attention mechanism could effectively identify important neighbor nodes and assign more flexible weight distributions, thereby better mining the hidden semantic information in the knowledge graph.
* Although RippleNet, KGNN-LS, KGAT, CKAN, KGIN, CAKR, KGG, KCNR, KGIE, and LKGA are all propagation-based recommendation methods, their performance on the Book-Crossing dataset varied significantly. This is primarily due to the unique characteristics of different datasets. During the process of information propagation, while useful information is obtained, noise is also introduced. Finding a balance between acquiring useful information and reducing noise is a direction for the continuous optimization of models. It is difficult to find a propagation-based method that performs well across all datasets, which also serves as the starting point for our  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{3}{c}{**Book-Crossing**} \\ \cline{2-3}
**Model** & **AUC** & **F1** \\ \hline BPRMF & 0.658 & 0.611 \\ CKE & 0.671 & 0.611 \\ PER & 0.624 & 0.562 \\ RippleNet & 0.725 & 0.650 \\ KGNN-LS & 0.676 & 0.631 \\ KGAT & 0.731 & 0.654 \\ CKAN & 0.742 & 0.667 \\ KGIN & 0.727 & 0.661 \\ CAKR & 0.744 & 0.648 \\ KGG & 0.731 & 0.668 \\ KCNR & 0.750 & 0.666 \\ KGIE & 0.729 & 0.653 \\ LKGA & 0.754 & 0.673 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The AUC and F1 values for each model.  
ongoing research into personalized learning resource recommendation models based on knowledge graphs.
* CKAN, CAKR, and our model all involve attention mechanisms to varying degrees to improve the performance of the models in tasks such as recommendation. The CAKR model focuses on information sharing between items and their corresponding entities. Compared to KGAT, the model's performance is improved to some extent, indicating that enhancing user and item representations is crucial for recommendation tasks.
* Our model fully leveraged collaborative signals to learn rich representations of books and learners and utilized initial connections to ensure information decay during the propagation process. This demonstrated that combining collaborative signals in the user-item interaction graph with auxiliary information in the knowledge graph was key to improving recommendation performance.