# A Knowledge Graph-Enhanced Attention Aggregation Network for Making Recommendations
## 4 Experiment and Results
### Results
#### 4.3.2 The Performance in CTR Prediction  
In CTR prediction, we used AUC (Area Under Curve) and ACC (Accuracy) to evaluate the performance of all models. In addition, we also evaluated the enhancement effect of the knowledge graph embedding model and the attention model on KANR, as shown in Table 2.  
KANR-KGE means that the model only trained the recommendation model and did not interact with the knowledge graph embedded model. KANR-ATT indicates that the model only used the average aggregation algorithm in the aggregation process of the user vector and the historical interaction item vector, and did not add the item weight calculated by the attention model.  
In CTR prediction, KANR slightly leads the baseline model in terms of AUC and ACC. In the movie dataset, KANR has certain performance advantages, whereas the AUC index in the music and book datasets has been significantly improved. However, it also lags slightly behind in the accuracy index of the book dataset. In addition, we can see  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & \multicolumn{2}{c}{**MovieLens-1M**} & \multicolumn{2}{c}{**LastFM**} & \multicolumn{2}{c}{**Book-Crossing**} \\ \hline  & **AUC** & **ACC** & **AUC** & **ACC** & **AUC** & **ACC** \\ \hline Wide\&Deep & 0.898 (\(-3.0\%\)) & 0.820 (\(-3.6\%\)) & 0.756 (\(-7.4\%\)) & 0.688 (\(-8.5\%\)) & 0.712 (\(-4.3\%\)) & 0.624 (\(-11.1\%\)) \\ CKE & 0.801 (\(-13.7\%\)) & 0.742 (\(-12.8\%\)) & 0.744 (\(-8.9\%\)) & 0.673 (\(-10.5\%\)) & 0.671 (\(-9.8\%\)) & 0.673 (\(-4.2\%\)) \\ LibFM & 0.892 (\(-3.9\%\)) & 0.812 (\(-4.5\%\)) & 0.777 (\(-4.8\%\)) & 0.709 (\(-5.7\%\)) & 0.685 (\(-7.9\%\)) & 0.640 (\(-8.6\%\)) \\ Ripple & 0.920 (\(-0.9\%\)) & 0.842 (\(-1.1\%\)) & 0.780 (\(-4.5\%\)) & 0.702 (\(-6.6\%\)) & 0.729 (\(-2.0\%\)) & 0.662 (\(-5.6\%\)) \\ MKR & 0.924 (\(-0.5\%\)) & 0.848 (\(-0.3\%\)) & 0.796 (\(-2.5\%\)) & 0.752 (\(0\%\)) & 0.738 (\(-0.8\%\)) & 0.688 (\(-1.9\%\)) \\ KGCN & 0.917 (\(-1.3\%\)) & 0.843 (\(-0.9\%\)) & 0.796 (\(-2.5\%\)) & 0.728 (\(-3.2\%\)) & 0.731 (\(-1.7\%\)) & 0.678 (\(-3.4\%\)) \\ GMCF & 0.918 (\(-1.1\%\)) & 0.845 (\(-0.7\%\)) & 0.785 (\(-3.9\%\)) & 0.71 (\(-2.3\%\)) & **0.789 (\(+6.0\%\))** & **0.712 (\(+1.4\%\))** \\ \hline KANR & **0.929** & **0.851** & **0.817** & **0.752** & 0.744 & 0.702 \\ KANR-K & 0.903 (\(-2.7\%\)) & 0.826(\(-2.9\%\)) & 0.782 (\(-4.3\%\)) & 0.742(\(-3.5\%\)) & 0.718 (\(-3.5\%\)) & 0.668 (\(-4.8\%\)) \\ KANR-A & 0.921 (\(-0.8\%\)) & 0.839(\(-1.4\%\)) & 0.808 (\(-1.1\%\)) & 0.746 (\(-0.8\%\)) & 0.726 (\(-2.4\%\)) & 0.679 (\(-3.2\%\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance of each model in CTR prediction. The best results are in bold.  
that by excluding the knowledge graph embedding module and the attention module, the performance of the three datasets shows different degrees of decline, which proves that the semantic information in the knowledge graph and the attention model provides a certain improvement in performance for the recommendation task. As a result of the enhancement of KANR, the information interaction unit of KANR can effectively share vector features.