Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph

Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph

IEEE Transactions on Knowledge and Data Engineering

10.1109/TKDE.2021.3082948

https://doi.org/10.1109/tkde.2021.3082948

Yong Liu, Susen Yang, Yonghui Xu, Chunyan Miao, Min Wu, Juyong Zhang

24 May 2021

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, 1–1. https://doi.org/10.1109/tkde.2021.3082948

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.

Publisher Copyright

Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute (JRI), Nanyang Technological University, Singapore.

This research / project is supported by the National Research Foundation - AI Singapore Programme
Grant Reference no. : AISG-GC-2019-003

This research / project is supported by the National Research Foundation - NRF Investigatorship Programme
Grant Reference no. : NRF-NRFI05-2019-0002

This research / project is supported by the the National Natural Science Foundation of China - NA
Grant Reference no. : 61672481

This research / project is supported by the Chinese Academy of Sciences (CAS) - Youth Innovation Promotion Association
Grant Reference no. : 2018495

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