KAT: knowledge-aware attentive recommendation model integrating two-terminal neighbor features.

Due to its ability to effectively address the cold start and sparsity problems in collaborative filtering, knowledge graph is commonly used as auxiliary information in recommendation systems. However, the existing recommendation algorithms based on knowledge graphs mainly focus on utilizing the connection structure to obtain user interests or item features, without emphasizing the simultaneous feature extraction on both the user and item sides. Therefore, the learned embeddings can not effectively represent the potential semantics of users and items. In this paper, we proposed KAT, a knowledge-aware attentive recommendation model integrating two-terminal neighbor features, which to extract fine-grained user and item features by alternating preference propagation and neighborhood information aggregation. The two modules automatically update and share entity embedding. Specifically, we introduce knowledge-aware attention mechanism to enhance the distinction of adjacent entities. Furthermore, we design a neighbor sampling mechanism to calculate the maximum node influence by extracting the largest connected subnet, which avoids the instability of the model performance caused by random sampling. We validate the effectiveness of KAT on four different datasets: movie, music, book, and grape (the latter is a dataset that we constructed through market research). Numerous experiments have demonstrated that KAT significantly outperforms several recent baselines, and AUC and ACC have increased by 2.81% and 1.28% respectively on our self-built dataset. [ABSTRACT FROM AUTHOR]