# Knowledge Graph Double Interaction Graph Neural Network for Recommendation Algorithm
## 5 Experiments
### Results and Analysis  
#### 5.3.1 Overall Comparison  
The performance comparison results are presented in Table 3.  
As can be seen from Table 3, LFM is the only recommendation model that does not use knowledge graphs among all recommendation algorithms in the comparative experiments. The LFM recommendation model lagged behind other recommendation methods on all datasets in the comparative experiments. The result verifies that the introduction of knowledge graph information can promote the recommendation quality improvement of RippleNet, KGNN-LS, KGCN, and KGDI. It also proves that KGDI has passed effectiveness of knowledge graph extracting item feature vectors. On the MoviesLens-1M dataset, RippleNet performed the best among the baseline models. This indicates that RippleNet can capture user interests, especially in the case of intensive user item interactions. However, RippleNet was more sensitive to the density of the dataset, as it performed worse than KGDI on Book-Crossing. KGNN-LS performed best among all baseline models on the Book-Crossing dataset. KGNN-LS uses a graph neural network to aggregate neighborhood information, which shows that capturing neighborhood information in knowledge graph is essential for recommendation.  
KGDI is always better than KGCN, because KGDI adds the influence weight of neighbor entities on the central entity when aggregating neighbor information; when aggregating entity information, the user is interacted with the item features and the aggregated neighbor features separately. This increases the information of the user and thus improves the learning of entity representation.  
Figures 5 and 6 show the Precision@K and Recall@K performance of Top-K recommendations for each model on the MoviesLens-1M dataset and Book-Crossing dataset, respectively.  
The KGDI model Recall@K performed more outstandingly on both datasets. On the MoviesLens-1M dataset, KGDI improved over the baseline model Recall@20 by 2.2-11.2%; on the Book-Crossing dataset, KGDI improved over the baseline model Recall@20 by 4.5-10.8%. In addition, the KGDI model performed better at K \(\geq\)  
Figure 5: MoviesLens-1M dataset on Top-K recommendation by Precision@K, Recall@K.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**MovieLens-1M**} & \multicolumn{2}{c}{**Book-Crossing**} \\ \cline{2-5}  & **AUC** & **F1** & **AUC** & **F1** \\ \hline LFM & 0.892 (\(-1.3\%\)) & 0.823 & 0.660 (\(-3.8\%\)) & 0.134 \\ RippleNet & 0.903 (\(-0.2\%\)) & 0.833 & 0.668 (\(-3\%\)) & 0.632 \\ KGNN-LS & 0.863 (\(-4.2\%\)) & 0.810 & 0.690 (\(-0.8\%\)) & 0.664 \\ KGCN & 0.880 (\(-2.5\%\)) & 0.798 & 0.675 (\(-2.3\%\)) & 0.625 \\ KGDI-GCN & 0.904 & 0.834 & 0.697 & 0.645 \\ KGDI-GS & 0.905 & 0.835 & 0.691 & 0.633 \\ KGDI-BI & 0.894 & 0.819 & 0.698 & 0.640 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of AUC and F1 in CTR prediction.  
dataset. KGDI improved over the baseline model Precision@20 by 1.3\(\sim\)3%. The KGDI model performed better at K \(\geq\) 10 in the Book-Crossing dataset and KGDI improved over the baseline model Precision@10 by 0.6\(\sim\)1.6%. Overall, although the performance of Precision@K and Recall@K was slightly lower than the baseline model in the Top-1 recommendation, this will not affect the overall performance of KGDI because KGDI performs better in other situations.  
The loss control curves for training and validation and the accuracy control curves for training and validation for the MoviesLens-1M dataset and the Book-Crossing dataset are shown in Figures 7 and 8.  
Figure 9 presents the t-SNE visualization of the embedding of the proposed structure.  
Figure 8: Loss curve and accuracy curve of Book-Crossing dataset.  
Figure 6: Book-Crossing dataset on Top-K recommendation by Precision@K, Recall@K.  
Figure 7: Loss curve and accuracy curve of MoviesLens-1M dataset.