# The Use of Attentive Knowledge Graph Perceptual Propagation for Improving Recommendations
## 5 Experiments
### Experimental Results  
#### 5.4.1 Performance Comparisons with Baselines  
Table 3 shows the experimental results of the AUC and F1 metrics of the AKGP algorithm and the benchmark algorithm in the click-through rate prediction scenario.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Optimizer & Adam \\ Learning Rate & 0.001 \\ Embed Dimension & 64 \\ Batch Size & 1024 \\ L2 weighs & \(1\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental settings.  
As seen in the combined experimental results of each algorithm using the three datasets, most of the recommendation algorithms that incorporated knowledge graphs outperform the classical collaborative filtering algorithms, which indicates the positive impact of external auxiliary information on the recommendation effect. The neural collaborative filtering-based recommendation algorithm NEUMF performs the worst of all the benchmark algorithms among the different models for each metric recommendation. This may be due to its difficulty in making recommendations for data that do not appear in the user-item interaction matrix. The result for the embedding-based knowledge graph recommendation algorithm (CKE) is slightly lower than that of BPRMF for most metrics. This could be because it only considers the structural information of the knowledge graph and ignores the influence of its domain information, so it cannot effectively explore the potential interests of users. The recommended performance of AKGP is excellent, especially in the Last-FM and Book-Crossing datasets. Compared with baseline models such as BPRMF, NEUMF, CKE, RippleNet, KGCN, etc., the experimental results of AKGP validate the advantages of the fine-grained modeling of knowledge information and the effectiveness of using attention networks and perceptual propagation methods. A comparison of the precision and recall metrics for the Top-K recommendation scenario is shown in Figures 6 and 7. It can be seen that AKGP outperforms other benchmark algorithms in terms of recall for multiple K values.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Last-FM**} & \multicolumn{2}{c}{**Book-Crossing**} & \multicolumn{2}{c}{**Movielens-20M**} \\ \cline{2-7}  & **AUC** & **F1** & **AUC** & **F1** & **AUC** & **F1** \\ \hline NEUMF & 0.725 (\(-12.5\%\)) & 0.645 (\(-14.8\%\)) & 0.631 (\(-14.9\%\)) & 0.603 (\(-8.5\%\)) & 0.904 (\(-7.3\%\)) & 0.906 (\(-2.1\%\)) \\ BPRMF & 0.766 (\(-7.6\%\)) & 0.692 (\(-6.6\%\)) & 0.665 (\(-10.4\%\)) & 0.612 (\(-7.1\%\)) & 0.903 (\(-7.4\%\)) & 0.913 (\(-1.3\%\)) \\ CKE & 0.743 (\(-10.3\%\)) & 0.675 (\(-8.9\%\)) & 0.678 (\(-8.6\%\)) & 0.620 (\(-5.9\%\)) & 0.912 (\(-6.5\%\)) & 0.906 (\(-2.1\%\)) \\ RippleNet & 0.778 (\(-6.2\%\)) & 0.702 (\(-5.3\%\)) & 0.720 (\(-2.9\%\)) & 0.645 (\(-2.1\%\)) & 0.921 (\(-5.5\%\)) & 0.920 (\(-0.5\%\)) \\ KGCN & 0.802 (\(-3.2\%\)) & 0.722 (\(-2.6\%\)) & 0.686 (\(-7.5\%\)) & 0.631 (\(-4.2\%\)) & 0.974 (\(-0.2\%\)) & 0.924 (\(-0.1\%\)) \\ KGAT & 0.825 (\(-0.4\%\)) & 0.739 (\(-0.3\%\)) & 0.725 (\(-2.3\%\)) & 0.654 (\(-0.7\%\)) & 0.972 (\(-0.4\%\)) & 0.922 (\(-0.3\%\)) \\ AKGP & 0.829 & 0.741 & 0.742 & 0.659 & 0.976 & 0.925 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparisons with baselines.  
Figure 6: The results of Precision in Top-K recommendation. (**a**) Results using the Last-FM dataset. (**b**) Results using the Book-Crossing dataset. (**c**) Results using the MovieLens-20M dataset.