KGAT: Knowledge Graph Attention Network for Recommendation KGAT is a novel method that leverages knowledge graphs (KGs) to enhance recommendation systems by explicitly modeling high-order relations in a collaborative knowledge graph (CKG). Traditional methods like factorization machines (FM) treat user-item interactions as independent instances, but they fail to capture the complex relationships between items and attributes. KGAT addresses this by integrating KGs with user-item graphs, allowing for the explicit modeling of high-order relations that connect items through multiple attributes. The proposed KGAT model uses an end-to-end approach to propagate embeddings from a node's neighbors (users, items, or attributes) to refine the node's representation. It employs an attention mechanism to determine the importance of each neighbor, enabling the model to focus on the most relevant connections. This approach allows KGAT to effectively capture collaborative signals from user behavior and item attributes, leading to more accurate and explainable recommendations. Empirical results on three public benchmarks show that KGAT significantly outperforms state-of-the-art methods like Neural FM and RippleNet. The model's effectiveness is further validated through extensive experiments, demonstrating its ability to capture high-order connectivity and provide interpretable explanations for recommendations. KGAT's key contributions include the explicit modeling of high-order relations in CKGs, the development of a new method that achieves end-to-end high-order relation modeling, and extensive experiments showing the effectiveness of KGAT in understanding the importance of high-order relations. The model's architecture includes an embedding layer, attentive embedding propagation layers, and a prediction layer, which work together to capture and propagate high-order information across the graph. The model's performance is evaluated using metrics such as recall@K and ndcg@K, with KGAT consistently outperforming other methods across different datasets and sparsity levels. The attention mechanism in KGAT also enables the model to provide explanations for recommendations, highlighting the importance of high-order connectivity in capturing user preferences.KGAT: Knowledge Graph Attention Network for Recommendation KGAT is a novel method that leverages knowledge graphs (KGs) to enhance recommendation systems by explicitly modeling high-order relations in a collaborative knowledge graph (CKG). Traditional methods like factorization machines (FM) treat user-item interactions as independent instances, but they fail to capture the complex relationships between items and attributes. KGAT addresses this by integrating KGs with user-item graphs, allowing for the explicit modeling of high-order relations that connect items through multiple attributes. The proposed KGAT model uses an end-to-end approach to propagate embeddings from a node's neighbors (users, items, or attributes) to refine the node's representation. It employs an attention mechanism to determine the importance of each neighbor, enabling the model to focus on the most relevant connections. This approach allows KGAT to effectively capture collaborative signals from user behavior and item attributes, leading to more accurate and explainable recommendations. Empirical results on three public benchmarks show that KGAT significantly outperforms state-of-the-art methods like Neural FM and RippleNet. The model's effectiveness is further validated through extensive experiments, demonstrating its ability to capture high-order connectivity and provide interpretable explanations for recommendations. KGAT's key contributions include the explicit modeling of high-order relations in CKGs, the development of a new method that achieves end-to-end high-order relation modeling, and extensive experiments showing the effectiveness of KGAT in understanding the importance of high-order relations. The model's architecture includes an embedding layer, attentive embedding propagation layers, and a prediction layer, which work together to capture and propagate high-order information across the graph. The model's performance is evaluated using metrics such as recall@K and ndcg@K, with KGAT consistently outperforming other methods across different datasets and sparsity levels. The attention mechanism in KGAT also enables the model to provide explanations for recommendations, highlighting the importance of high-order connectivity in capturing user preferences.