# Knowledge Graph Recommendation Model Based on Feature Space Fusion
## 4 Experiments
### Study of KGRFSF  
#### 4.5.1 Influence of Network Layers  
This section compares the influence of different network layers on the recommendation results of the behavioral feature module and content feature module through experiments.  
First, the network layers of the content feature module are fixed as 1, and the network layers of the behavioral feature module are adjusted as 1, 2, 3, and 4. The experimental results are shown in Figure 4. The results show that the model obtains the best result when the behavioral feature module network has 3 layers and the content feature module network has 1 layer. This shows that fully mining the behavior information in the UIG is helpful to accurately model the behavioral features of users and items and enhance the model performance. When the network layers of behavioral feature modules are small, the model cannot fully extract behavioral features. However, if the selected neighborhood scope is too large, information with a low degree of association with users and items will be introduced, resulting in the decline in AUC on the Last.FM dataset and no further improvement in AUC on the MovieLens-20 M dataset.  
Figure 4: Influence of behavioral feature layers on AUC.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Last.FM**} & \multicolumn{2}{c}{**MovieLens-20 M**} \\ \cline{2-5}  & **AUC** & **F1** & **AUC** & **F1** \\ \hline CKE & 0.747 & 0.674 & 0.927 & 0.874 \\ KGCN & 0.802 & 0.708 & 0.977 & 0.930 \\ KGAT & 0.829 & 0.742 & 0.975 & 0.929 \\ CKAN & 0.842 & 0.769 & 0.976 & 0.929 \\ KGRFSF & 0.862 & 0.799 & 0.982 & 0.931 \\ Improve/\% & 2\% & 3\% & 0.6\% & 0.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recommendation accuracy comparison experiment results.  
Next, the network layers of the content feature module are adjusted on the basis of the behavioral feature module network taking 3 layers, and the size of AUC is compared. The results are shown in Figure 5. By observing the experimental results, it can be found that AUC reduces gradually with the rise in network layers of content feature modules. The reason for this result is that the first-order neighbor in the knowledge graph can most accurately describe the content features of users and items, while the higher-order neighbor entity contains content features with low relevance to users and items, which interferes with recommendations. Therefore, when extracting content features in the knowledge graph, better results can be obtained by simplifying network layers.  
In conclusion, aggregation of high-order neighborhood information is helpful to accurately obtain the behavioral features of users and items in the UIG. However, if the selected neighborhood scope is too large, information with a low correlation degree with the central node will be introduced. Experiments show that the model can obtain the best recommendation performance when the behavioral feature module network is at 3 layers. In the knowledge graph, first-order neighbors are most closely related to users and items, while higher-order neighbors may contain content irrelevant to users and items that interfere with recommendations. It can be found through the experimental results that the model can attain the best recommendation performance when the content feature module network is set to 1 layer.