This is the PyTorch implementation for LightGCL proposed in the paper Simple Yet Effective Graph Contrastive Learning for Recommendation, International Conference on Learning Representation, 2023.
Due to the large size of datasets ML-10M, Amazon and Tmall, we have compressed them into zip files. Please unzip them before running the model on these datasets. For Yelp and Gowalla, keeping the current directory structure is fine.
Before running the codes, please ensure that two directories log/
and saved_model/
are created under the root directory. They are used to store the training results and the saved model and optimizer states.
We develope our codes in the following environment:
Python version 3.9.12
torch==1.12.0+cu113
numpy==1.21.5
tqdm==4.64.0
- Yelp
python main.py --data yelp
- Gowalla
python main.py --data gowalla --lambda2 1e-5 --temp 0.3
- ML-10M
python main.py --data ml10m --temp 10
- Amazon
python main.py --data amazon --lambda1 1e-5 --temp 0.1
- Tmall
python main.py --data tmall --lambda1 1e-6 --temp 0.3 --dropout 0
-
--cuda
specifies which GPU to run on if there are more than one. -
--data
selects the dataset to use. -
--lambda1
specifies$\lambda_1$ , the regularization weight for CL loss. -
--lambda2
is$\lambda_2$ , the L2 regularization weight. -
--temp
specifies$\tau$ , the temperature in CL loss. -
--dropout
is the edge dropout rate. -
--q
decides the rank q for SVD.