MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems
This is our PyTorch implementation for the paper:
Tinglin Huang, Yuxiao Dong, Ming Ding, Zhen Yang, Wenzheng Feng, Xinyu Wang, Jie Tang (2021). MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems. Paper link. In KDD'2021, Virtual Event, Singapore, August 14-18, 2021.
Author: Mr. Tinglin Huang (tinglin.huang at zju.edu.cn)
Citation
If you want to use our codes in your research, please cite:
@inproceedings{MixGCF2021,
author = {Tinglin Huang and
Yuxiao Dong and
Ming Ding and
Zhen Yang and
Wenzheng Feng and
Xinyu Wang and
Jie Tang},
title = {MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems},
booktitle = {{KDD}},
year = {2021}
}
Environment Requirement
The code has been tested running under Python 3.7.6. The required packages are as follows:
- pytorch == 1.7.0
- numpy == 1.20.2
- scipy == 1.6.3
- sklearn == 0.24.1
- prettytable == 2.1.0
Training
The instruction of commands has been clearly stated in the codes (see the parser function in utils/parser.py). Important argument:
K- It specifies the number of negative instances in K-pair loss. Note that when K=1 (by default), the K-pair loss will degenerate into the BPR pairwise loss.
n_negs- It specifies the size of negative candidate set when using MixGCF.
ns- It indicates the type of negative sample method. Here we provide two options: rns and mixgcf.
LightGCN
Random sample(rns)
python main.py --dataset ali --gnn lightgcn --dim 64 --lr 0.001 --batch_size 2048 --gpu_id 0 --context_hops 3 --pool mean --ns rns --K 1 --n_negs 1
python main.py --dataset yelp2018 --gnn lightgcn --dim 64 --lr 0.001 --batch_size 2048 --gpu_id 0 --context_hops 3 --pool mean --ns rns --K 1 --n_negs 1
python main.py --dataset amazon --gnn lightgcn --dim 64 --lr 0.001 --batch_size 2048 --gpu_id 0 --context_hops 3 --pool mean --ns rns --K 1 --n_negs 1
MixGCF
python main.py --dataset ali --dim 64 --lr 0.001 --batch_size 2048 --gpu_id 0 --context_hops 3 --pool mean --ns mixgcf --K 1 --n_negs 32
python main.py --dataset yelp2018 --dim 64 --lr 0.001 --batch_size 2048 --gpu_id 0 --context_hops 3 --pool mean --ns mixgcf --K 1 --n_negs 64
python main.py --dataset amazon --dim 64 --lr 0.001 --batch_size 2048 --gpu_id 0 --context_hops 3 --pool mean --ns mixgcf --K 1 --n_negs 16
NGCF
Random sample(rns)
python main.py --dataset ali --gnn ngcf --dim 64 --lr 0.0001 --batch_size 1024 --gpu_id 0 --context_hops 3 --pool concat --ns rns --K 1 --n_negs 1
python main.py --dataset yelp2018 --gnn ngcf --dim 64 --lr 0.0001 --batch_size 1024 --gpu_id 0 --context_hops 3 --pool concat --ns rns --K 1 --n_negs 1
python main.py --dataset amazon --gnn ngcf --dim 64 --lr 0.0001 --batch_size 1024 --gpu_id 0 --context_hops 3 --pool concat --ns rns --K 1 --n_negs 1
MixGCF
python main.py --dataset ali --gnn ngcf --dim 64 --lr 0.0001 --batch_size 1024 --gpu_id 0 --context_hops 3 --pool concat --ns mixgcf --K 1 --n_negs 64
python main.py --dataset yelp2018 --gnn ngcf --dim 64 --lr 0.0001 --batch_size 1024 --gpu_id 0 --context_hops 3 --pool concat --ns mixgcf --K 1 --n_negs 64
python main.py --dataset amazon --gnn ngcf --dim 64 --lr 0.0001 --batch_size 1024 --gpu_id 0 --context_hops 3 --pool concat --ns mixgcf --K 1 --n_negs 64
The training log is also provided. The results fluctuate slightly under different running environment.
Dataset
We use three processed datasets: Alibaba, Yelp2018, and Amazon.
| Alibaba | Yelp2018 | Amazon | |
|---|---|---|---|
| #Users | 106,042 | 31,668 | 192,403 |
| #Items | 53,591 | 38,048 | 63,001 |
| #Interactions | 907,407 | 1,561,406 | 1,689,188 |
| Density | 0.00016 | 0.00130 | 0.00014 |