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.

该研究提出了一种名为 CGAT 的新型推荐框架，可以明确地利用实体在知识图中的本地和非本地图上下文信息，以实现个性化的推荐，这可以通过一种偏向性随机游走采样过程来提取实体的非本地上下文，并利用 RNN 来模拟实体和其非本地上下文实体之间的依赖关系