Contextualized Graph Attention Network for Recommendation With Item Knowledge Graph

Graph neural networks (GNN) have recently been applied to exploit knowledge graph (KG) for recommendation. Existing GNN-based methods explicitly model the dependency between an entity and its local graph context in KG (i.e., the set of its first-order neighbors), but may not be effective in capturing its non-local graph context (i.e., the set of most related high-order neighbors). In this paper, we propose a novel recommendation framework, named Contextualized Graph Attention Network (CGAT), which can explicitly exploit both local and non-local graph context information of an entity in KG. More specifically, CGAT captures the local context information by a user-specific graph attention mechanism, considering a user's personalized preferences on entities. In addition, CGAT employs a biased random walk sampling process to extract the non-local context of an entity, and utilizes a Recurrent Neural Network (RNN) to model the dependency between the entity and its non-local contextual entities. To capture the user's personalized preferences on items, an item-specific attention mechanism is also developed to model the dependency between a target item and the contextual items extracted from the user's historical behaviors. We compared CGAT with state-of-the-art KG-based recommendation methods on real datasets, and the experimental results demonstrate the effectiveness of CGAT.

Automated summary

This paper proposes a recommendation framework called CGAT, which explicitly utilizes both local and non-local graph context information of entities in a knowledge graph. CGAT captures local context information using a user-specific graph attention mechanism, and extracts non-local context using biased random walk sampling process and models the dependency using an RNN. It also incorporates an item-specific attention mechanism to capture the user's personalized preferences. Experimental results on real datasets demonstrate the effectiveness of CGAT compared to state-of-the-art KG-based recommendation methods.