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Title: Contextualized graph attention network for recommendation with item knowledge graph
Authors: Liu, Yong
Yang, Susen
Xu, Yonghui
Miao, Chunyan
Wu, Min
Zhang, Juyong
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Liu, Y., Yang, S., Xu, Y., Miao, C., Wu, M. & Zhang, J. (2021). Contextualized graph attention network for recommendation with item knowledge graph. IEEE Transactions On Knowledge and Data Engineering.
Project: AISG-GC-2019-003 
Journal: IEEE Transactions on Knowledge and Data Engineering 
Abstract: 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.
ISSN: 1041-4347
DOI: 10.1109/TKDE.2021.3082948
Schools: School of Computer Science and Engineering 
Research Centres: Alibaba-NTU Singapore Joint Research Institute
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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