# CKGAT: Collaborative Knowledge-Aware Graph Attention Network for Top-N Recommendation
## 5 Experiments
### Comparison Methods  
In order to evaluate the effectiveness of our CKGAT method, we chose experimental comparison methods according to the following three rules. * In accordance with the practice of choosing experimental comparison methods in the existing research work [2, 3, 4, 5, 6, 7, 8, 15, 21, 23, 24, 28, 29, 30, 33, 34] in the field, we chose one classical collaborative filtering method, one typical embedding-based recommendation method, and six representative propagation-based recommendation methods (as the mainstream methods of knowledge graph-based recommendation);
* Unlike the above-mentioned research works, which did not choose a connection-based recommendation method as an experimental comparison method, we chose KPRN, a typical connection-based recommendation method, as the comparison method;
* We excluded recommendation methods whose codes were not available at the time of writing this paper, such as the methods in [24, 28, 29, 30, 33, 34]. As a consequence, CKGAT was compared with nine representative methods which are divided into four categories: collaborative filtering method (BPRMF), embedding-based recommendation method (CKE), connection-based recommendation method (KPRN), and propagation-based recommendation methods which are further divided into the user representation-refinement approaches (RippleNet and CKAN), the item representation-refinement approaches (KGCN and KGNN-LS), and both user and item representation-refinements approaches (KGAT and KGIN). Below are brief descriptions of these comparison methods. * **BPRMF**[45]. This method is a Bayesian personalized ranking (BPR) optimized matrix factorization (MF) model achieved by applying LearnBPR to MF. * **CKE**[2]. This method is a typical knowledge graph embedding-based recommendation method that combines the structural knowledge, textual knowledge and visual knowledge of items to learn item representations. * **KPRN**[3]. This method is a typical connection-based recommendation method that generates path representations by combining the semantics of entities and relations and distinguishes the importance of different paths, thereby capturing user preferences. * **RippleNet**[23]. This method is a classical propagation-based recommendation method that enhances user representations by propagating users' potential preferences in the knowledge graph. * **CKAN**[4]. This method belongs to the propagation-based recommendation methods that uses a heterogeneous propagation strategy and an attention network to learn ripple set embeddings, thereby generating user embeddings and item embeddings. * **KGCN**[5]. This method belongs to the propagation-based recommendation methods, which applies the graph convolutional network to the knowledge graph to aggregate neighborhood information to refine item representations. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Datasets**} & \multicolumn{1}{c}{**Last.FM**} & \multicolumn{1}{c}{**Book-Crossing**} & \multicolumn{1}{c}{**MovieLens 20M**} & \multicolumn{1}{c}{**Dianping-Food**} \\ \hline user-item & \#users & 1872 & 17,860 & 138,159 & 2,298,698 \\ interactions & \#items & 3846 & 14,967 & 16,954 & 1362 \\  & \#interactions & 42,346 & 139,746 & 13,501,622 & 23,416,418 \\ \hline knowledge & \#entities & 9366 & 77,903 & 102,569 & 28,115 \\ graph & \#relations & 60 & 25 & 32 & 7 \\  & \#triples & 15,518 & 151,500 & 499,474 & 160,519 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of the experimental datasets and their corresponding knowledge graphs. * **KGNN-LS [7]. ** This method belongs to the propagation-based recommendation methods that adds a label-smoothness mechanism to the KGCN framework to propagate user-interaction labels, so as to provide effective recommendations. * **KGAT**[8]. This method belongs to the propagation-based recommendation methods that applies the graph attention network to the collaborative knowledge graph to learn user representations and item representations. * **KGIN**[6].