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:

• tensorflow == 1.8.0

• numpy == 1.14.3

• scipy == 1.1.0

• sklearn == 0.19.1

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).

• Gowalla dataset

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]

• Amazon-book dataset

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:

• alg_type

• ngcf (by default), proposed in Neural Graph Collaborative Filtering, SIGIR2019. Usage: --alg_type ngcf.

• gcn, proposed in Semi-Supervised Classification with Graph Convolutional Networks, ICLR2018. Usage: --alg_type gcn.

• gcmc, propsed in Graph Convolutional Matrix Completion, KDD2018. Usage: --alg_type gcmc.

• It specifies the type of graph convolutional layer.

• Here we provide three options:

• adj_type

• ngcf (by default), where each decay factor between two connected nodes is set as 1(out degree of the node), while each node is also assigned with 1 for self-connections. Usage: --adj_type ngcf.

• plain, where each decay factor between two connected nodes is set as 1. No self-connections are considered. Usage: --adj_type plain.

• norm, where each decay factor bewteen two connected nodes is set as 1/(out degree of the node + self-conncetion). Usage: --adj_type norm.

• gcmc, where each decay factor between two connected nodes is set as 1/(out degree of the node). No self-connections are considered. Usage: --adj_type gcmc.

• It specifies the type of laplacian matrix where each entry defines the decay factor between two connected nodes.

• Here we provide four options:

• node_dropout

• It indicates the node dropout ratio, which randomly blocks a particular node and discard all its outgoing messages. Usage: --node_dropout [0.1] --node_dropout_flag 1

• Note that the arguement node_dropout_flag also needs to be set as 1, since the node dropout could lead to higher computational cost compared to message dropout.

• mess_dropout

• It indicates the message dropout ratio, which randomly drops out the outgoing messages. Usage --mess_dropout [0.1,0.1,0.1].

Dataset

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

• train.txt

• Train file.

• Each line is a user with her/his positive interactions with items: userIDt a list of itemIDn.

• test.txt

• Test file (positive instances).

• Each line is a user with her/his positive interactions with items: userIDt a list of itemIDn.

• Note that here we treat all unobserved interactions as the negative instances when reporting performance.

• user_list.txt

• User file.

• Each line is a triplet (org_id, remap_id) for one user, where org_id and remap_id represent the ID of the user in the original and our datasets, respectively.

• item_list.txt

• Item file.

• Each line is a triplet (org_id, remap_id) for one item, where org_id and remap_id represent the ID of the item in the original and our datasets, respectively.