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

Thi Linh Hoang, Tuan Dung Pham, Viet Cuong Ta
1/12/2024
cs.LGcs.AIcs.IR

No official code found. Best implementation: pytergeist/GCN-Transformer-Network (7 stars, last commit 2024-11-20). 1 more below.

Abstract

In this work, we have proposed an approach for improving the GCN for predicting ratings in social networks. Our model is expanded from the standard model with several layers of transformer architecture. The main focus of the paper is on the encoder architecture for node embedding in the network. Using the embedding layer from the graph-based convolution layer, the attention mechanism could rearrange the feature space to get a more efficient embedding for the downstream task. The experiments showed that our proposed architecture achieves better performance than GCN on the traditional link prediction task.

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Code Implementations(2)

linhthi/tsCommunity100%

Codebase for paper: "Improving GCN with Transformer layer in social-based items recommendation"

51Python, Jupyter NotebookSep 18, 20205 years ago
graph-convolutional-networksgraph-neural-networksgraph-transformerrecommendationstransformer
pytergeist/GCN-Transformer-NetworkCommunity66%

A repository to implament the GCN-Transformer network from the paper "Improving Graph Convolutional Networks with Transformer Layer in social-based items recommendation"

70Shell, PythonNov 18, 20241 years ago

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Cite this paper

@article{hoang2024improving,
  title  = {Improving Graph Convolutional Networks with Transformer Layer in social-based items recommendation},
  author = {Thi Linh Hoang and Tuan Dung Pham and Viet Cuong Ta},
  year   = {2024},
  eprint = {2401.06436},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.06436v1}
}

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