2 research outputs found
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.
Specifically, CGAT captures the local context information by a user-specific
graph attention mechanism, considering a user's personalized preferences on
entities. Moreover, 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. Experimental results on real datasets demonstrate the
effectiveness of CGAT, compared with state-of-the-art KG-based recommendation
methods