Improving Graph Convolutional Networks with Transformer Layer in social-based items recommendation

Hoang, Thi Linh and Pham, Tuan Dung and Ta, Viet Cuong (2021) Improving Graph Convolutional Networks with Transformer Layer in social-based items recommendation. In: The 13th International Conference on KNOWLEDGE AND SYSTEMS ENGINEERING (KSE 2021), 10-12 Nov 2021, Bangkok, Thailand. (Submitted)

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Abstract

With the emergence of online social networks, social-based items recommendation has become a popular research direction. Recently, Graph Convolutional Networks have shown promising results by modeling the information diffusion process in graphs. It provides a unified framework for graph embedding that can leverage both the social graph structure and node features information. In this paper, we improve the embedding output of the graph-based convolution layer by adding a number of transformer layers. The transformer layers with attention architecture help discover frequent patterns in the embedding space which increase the predictive power of the model in the downstream tasks. Our approach is tested on two social-based items recommendation dataset, Ciao and Epinions and our model outperforms other graph-based recommendation baselines.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Information Technology (IT)
Divisions:	Faculty of Information Technology (FIT)
Depositing User:	Thi Linh Hoang
Date Deposited:	10 Dec 2021 01:13
Last Modified:	10 Dec 2021 01:13
URI:	http://eprints.uet.vnu.edu.vn/eprints/id/eprint/4670

Available Versions of this Item

• Improving Graph Convolutional Networks with Transformer Layer in social-based items recommendation. (deposited 10 Dec 2021 01:13) [Currently Displayed]
  • Improving Graph Convolutional Networks with Transformer Layer in social-based items recommendation. (deposited 10 Dec 2021 10:57)

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