## 4 Experiments
### Performance Comparison
#### 4.2.2 Performance Comparison of the Knowledge Graph Recommendation Model  
The ATKGRM is compared with seven baseline knowledge graph recommendation models on the same three public datasets. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **N** & **d** & **H** & **L** & \(\mathbf{\lambda_{1}}\) & **Batch** & **lr** \\ \hline MovieLens-1M & 4 & 32 & 1 & 5 & \(2\times 10^{-5}\) & 256 & \(5\times 10^{-4}\) \\ MovieLens-10M & – & 32 & – & 5 & \(2\times 10^{-5}\) & 256 & \(2\times 10^{-4}\) \\ MovieLens-20M & 4 & 32 & 2 & 7 & \(10^{-7}\) & 65,536 & \(2\times 10^{-2}\) \\ Book-Crossing & 4 & 32 & 1 & 5 & \(2\times 10^{-5}\) & 256 & \(2\times 10^{-4}\) \\ Last. FM & 4 & 32 & 1 & 5 & \(10^{-4}\) & 128 & \(5\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental parameter setting. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **MovieLens-1M** & **MovieLens-10M** \\ \hline CF-NADE & 0.829 & 0.771 \\ NCAE & – & 0.767 \\ NESVD & 0.826 & 0.762 \\ ATKGRM-KG & 0.511 & 0.441 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RMSE of different models on MovieLens-1M and MovieLens-10M. The seven baselines knowledge graph recommendation models are introduced below:  
* CKE [36] uses TransE [8] to preprocess entity and relationship information in KG to model user and item features;
* PER [21] extracts the information of meta-paths in a knowledge graph to show the connectivity between items and users on diverse types of relationship paths;
* RippleNet [23] uses multi-hop neighborhood information to expand a user's potential interests by iteratively and autonomously following the links in KG;
* KGCN [24] uses a graph convolution network to automatically obtain semantic information and a high-order structure by sampling definite neighbors as the receptive field of candidate items;
* KGAT [28] combines a user-item interaction graph with KG as a collaborative knowledge graph; in communication, an attention module is used to differentiate the significance of neighborhood entities in a collaborative knowledge graph;
* HAGERec [29] uses the convolution network of a hierarchical attention graph to process KGs and explore the potential preferences of users; to fully use semantic information, a two-way information dissemination strategy is used to obtain the vector of items and users;
* CKAN [30] uses the cooperative knowledge perception attention module to differentiate the contributions of diverse neighborhood entities in processing KG and collaborative information. From Table 4 (or Figure 3), the AUC of ATKGRM in MovieLens-20M, Book-Crossing and Last.FM is increased by 0.4%, 2.5%, and 2.0%, respectively, and the F1 increases by 2.3%, 3.1%, and 0.5%, respectively. ATKGRM shows reliable performance. It is almost close to KGAT and HAGERec with strong performance, and it obtains the best performance in the value of individual K. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MovieLens-20M**} & \multicolumn{2}{c}{**Book-Crossing**} & \multicolumn{2}{c}{**Last. FM**} \\ \cline{2-7}  & **AUC** & **F1** & **AUC** & **F1** & **AUC** & **F1** \\ \hline PER & 0.832 & 0.733 & 0.623 & 0.588 & 0.633 & 0.596 \\ CKE & 0.924 & 0.880 & 0.671 & 0.633 & 0.744 & 0.673 \\ RippleNet & 0.968 & 0.924 & 0.729 & 0.662 & 0.768 & 0.691 \\ KGCN & 0.978 & 0.932 & 0.688 & 0.630 & 0.796 & 0.708 \\ KGAT & 0.976 & 0.925 & 0.731 & 0.658 & 0.829 & 0.740 \\ HAGERec & 0.980 & 0.933 & 0.763 & 0.701 & 0.814 & 0.743 \\ CKAN & 0.976 & 0.930 & 0.753 & 0.675 & 0.840 & 0.772 \\ ATKGRM & 0.984 & 0.954 & 0.782 & 0.723 & 0.857 & 0.776 \\ Improve/\% & 0.4\% & 2.3\% & 2.5\% & 3.1\% & 2.0\% & 0.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison results of knowledge graph recommendation models. Figure 3: Comparison results of knowledge graph recommendation models. (On three different datasets).