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Neural Graph Collaborative Filtering

This is our Tensorflow implementation for the paper:

Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua (2019). Neural Graph Collaborative Filtering, Paper in ACM DL or Paper in arXiv. In SIGIR'19, Paris, France, July 21-25, 2019.

Author: Dr. Xiang Wang (xiangwang at u.nus.edu)

Introduction

Neural Graph Collaborative Filtering (NGCF) is a new recommendation framework based on graph neural network, explicitly encoding the collaborative signal in the form of high-order connectivities in user-item bipartite graph by performing embedding propagation.

Citation

If you want to use our codes and datasets in your research, please cite:

@inproceedings{NGCF19,
  author    = {Xiang Wang and
               Xiangnan He and
               Meng Wang and
               Fuli Feng and
               Tat{-}Seng Chua},
  title     = {Neural Graph Collaborative Filtering},
  booktitle = {Proceedings of the 42nd International {ACM} {SIGIR} Conference on
               Research and Development in Information Retrieval, {SIGIR} 2019, Paris,
               France, July 21-25, 2019.},
  pages     = {165--174},
  year      = {2019},
}

Environment Requirement

The code has been tested running under Python 3.6.5. The required packages are as follows:

Example to Run the Codes

The instruction of commands has been clearly stated in the codes (see the parser function in NGCF/utility/parser.py).

python NGCF.py --dataset gowalla --regs [1e-5] --embed_size 64 --layer_size [64,64,64] --lr 0.0001 --save_flag 1 --pretrain 0 --batch_size 1024 --epoch 400 --verbose 1 --node_dropout [0.1] --mess_dropout [0.1,0.1,0.1]
python NGCF.py --dataset amazon-book --regs [1e-5] --embed_size 64 --layer_size [64,64,64] --lr 0.0005 --save_flag 1 --pretrain 0 --batch_size 1024 --epoch 200 --verbose 50 --node_dropout [0.1] --mess_dropout [0.1,0.1,0.1]

Some important arguments:

Dataset

We provide two processed datasets: Gowalla and Amazon-book.

Acknowledgement

This research is supported by the National Research Foundation, Singapore under its International Research Centres in Singapore Funding Initiative. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.