UltraGCN: An Ultra Simplification of Graph Convolutional Networks for Recommendation

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Deep LearningUltraGCN
Overview

UltraGCN

This is our Pytorch implementation for our CIKM 2021 paper:

Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, Xiuqiang He. UltraGCN: An Ultra Simplification of Graph Convolutional Networks for Recommendation. Paper in arXiv.

Introduction

In this work, we propose an ultra-simplified formulation of GCN, dubbed UltraGCN. UltraGCN skips explicit message passing and directly approximate the limit of infinite message passing layers.

Environment Requirement

The required packages are as follows:

  • python: 3.7.9
  • pytorch 1.4.0
  • numpy: 1.19.2
  • scipy: 1.1.0
  • tensorboard: 2.4.0

Code

  • main.py: All python code to reproduce UltraGCN
  • dataset_name_config.ini: The configuration file which includes parameter settings for reproduction on the specific dataset.
python main.py --config_file dataset_config.ini

Reproduction

See benchmarks folder to reproduce the results. For example, we show the detailed reproduce steps for the results of UltraGCN on the AmazoonBooks dataset in UltraGCN_amazonbooks_x0.md file.

Results

Model AmazonBooks AmazonBooks Yelp2018 Yelp2018 Gowalla Gowalla
[email protected] [email protected] [email protected] [email protected] [email protected] [email protected]
NGCF 0.0344 0.0263 0.0579 0.0477 0.1570 0.1327
LightGCN 0.0411 0.0315 0.0649 0.0530 0.1830 0.1554
UltraGCN 0.0681 0.0556 0.0683 0.0561 0.1862 0.1580
Owner
XUEPAI
XUEPAI
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