# Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative Filtering
## 4 Analysis of Experimental Data and Results
### Algorithm Comparison  
In order to verify the practicality of the methods used in this article, four other recommended methods were selected for comparison, namely TransD, ItemCF, CKE, and KGAT. In order to ensure the comprehensiveness of the experimental comparison, the baseline method selected in this article includes traditional recommendation algorithms and more advanced recommendation algorithms. Its introduction is as follows:  
1. TransD [42]: A model used for knowledge graph representation learning aimed at capturing semantic associations between entities and relationships.
2. ItemCF [43] (item-based collaborative filtering): Object-based collaborative filtering that analyzes items that users have liked in the past and recommends other items that are similar to these items.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\alpha\) & \multicolumn{3}{c}{**Video Dominance**} & \multicolumn{3}{c}{**Text Dominance**} \\ \hline  & Precision & Recall & F1 & Precision & Recall & F1 \\ \(\alpha=0\) & 0.5567 & 0.2788 & 0.3716 & 0.5568 & 0.2788 & 0.3716 \\ \(\alpha=0.1\) & 0.6300 & 0.3646 & 0.4619 & 0.6288 & 0.3617 & 0.4592 \\ \(\alpha=0  
* CKE [44] (Collaborative Knowledge Base Embedding): Based on knowledge graph embedding, a multi-layer neural network is used to integrate the knowledge base.
* KGAT [45] (Knowledge Graph Attention Network): It combines graph convolutional networks and attention mechanisms to capture complex associations in the knowledge graph.  
ItemCF-Text represents ItemCF using text dominant preference rules in this article. ItemCF-Video represents ItemCF using video dominant preference rules in this article. In the CKE algorithm, the interaction event that occurs in the dataset is denoted as 1; otherwise, it is denoted as 0. In the KGAT algorithm, each user's interaction with an item is considered a positive instance. The method used in this article \(\alpha\) Set to 0.6  
The experimental results are shown in Table 4.  
Analyzing the experimental results can lead to the following conclusions:  
* Compared with other models, the method proposed in this paper shows a certain improvement in performance. This indicates that the method used in this article relies more on the specific interaction data during the learning process. By introducing implicit feedback models, this article can better capture the feature representations of students' learning progress and resource types, especially when setting weights for different resource types. This means that we have a better understanding of student's needs and learning environments and can more accurately recommend appropriate learning resources for them.
* Compared to the KGAT and CKE methods, the method proposed in this paper slightly improves the F1 value. This may be because the task of this article focuses more on considering the interaction between users and resources, which can more easily adapt to the set preference rules. In addition, the complexity of the method proposed in this article is relatively low, which may also contribute to performance improvement.
* Compared with ItemCF and TransD, regardless of the K value, the mixed method exhibits better performance than using either method alone. This emphasizes the suitability of combining these two methods for recommendation in this article, providing better performance for recommendation systems.  
In summary, in this experiment, a hybrid method combining implicit feedback models, knowledge graphs, and collaborative filtering has been proven to be more suitable and suitable for the current experimental environment. This method takes into account students' learning preferences and fully utilizes existing learning interaction data, providing students with more intelligent and practical learning resources.