# CKGAT: Collaborative Knowledge-Aware Graph Attention Network for Top-N Recommendation
Zhuoming Xu  
College of Computer and Information, Hohai University, Nanjing 210098, China; [EMAIL_ADDRESS] (H.L. ); [EMAIL_ADDRESS] (U.L. ); [EMAIL_ADDRESS] (Q.Z. ); [EMAIL_ADDRESS] (Y.T.) Zhuoming Xu  
College of Computer and Information, Hohai University, Nanjing 210098, China; [EMAIL_ADDRESS] (H.L. ); [EMAIL_ADDRESS] (U.L. ); [EMAIL_ADDRESS] (Q.Z. ); [EMAIL_ADDRESS] (Y.T.) Zhuoming Xu  
College of Computer and Information, Hohai University, Nanjing 210098, China; [EMAIL_ADDRESS] (H.L. ); [EMAIL_ADDRESS] (U.L. ); [EMAIL_ADDRESS] (U.L. ); [EMAIL_ADDRESS] (Q.Z. ); [EMAIL_ADDRESS] (Y.T.) Zhuoming Xu  
College of Computer and Information, Hohai University, Nanjing 210098, China; hanlin. knowledge in the knowledge graph is utilized, knowledge graph-based recommendation methods can be classified into three categories [1]: embedding-based recommendation methods, connection-based recommendation methods, and propagation-based recommendation methods. The core idea of the embedding-based recommendation methods, such as collaborative knowledge-base embedding (CKE) [2], is leveraging the fruitful facts in the knowledge graph to enrich the representations of users and item. The core idea of connection-based recommendation methods, such as knowledge-aware path recurrent network (KPRN) [3], is using the connection patterns between entities (users/items) in the knowledge graph to guide recommendation. The core idea of the propagation-based recommendation methods, such as collaborative knowledge-aware attentive network (CKAN) [4], knowledge graph convolutional networks (KGCN) [5], and knowledge graph-based intent network (KGIN) [6], is integrating entity representations, relation representations, and high-order connection patterns between entities to provide more personalized recommendation. For a more comprehensive review of knowledge graph-based recommender systems, we refer readers to [1]. As commented in [1], the embedding-based recommendation methods are difficult to capture the high-order relations between entities, whereas the connection-based recommendation methods tend to lose information by decomposing the sophisticated connection pattern into separate linear paths. The propagation-based recommendation methods aim to make full use of the information in the knowledge graph, and thus become the mainstream methods for knowledge graph-based recommender systems. These methods adopt the idea of embedding propagation [7; 8], and usually employ an architecture based on graph neural network [9] to refine entity representations by aggregating the embeddings of multi-hop neighbors in the knowledge graph to better predict user preferences for items. For example, CKAN, a typical state-of-the-art propagation-based recommendation method, combines user-item interactions and knowledge associations in the knowledge graph, and performs heterogeneous propagation in the knowledge graph to generate multi-hop ripple sets, thereby capturing users' potential interests. However, existing propagation-based recommendation methods, including CKAN, usually ignore the complex relations between entities in the multi-hop ripple sets [5; 10] and do not distinguish the importance of different ripple sets, resulting in inaccurate user potential interests being captured. In order to capture more accurate user potential interests in items, which is the key to improving the performance of personalized recommendation, this paper proposes a top-N recommendation method named collaborative knowledge-aware graph attention network (CKGAT), which consists of three layers: heterogeneous propagation, knowledge-aware graph attention network (GAT)-based attentive embedding, and user-item interaction probability prediction.