# LightGCL
**Repository Path**: koalaaaaaaaaa/LightGCL
## Basic Information
- **Project Name**: LightGCL
- **Description**: 克隆自github——
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-02-23
- **Last Updated**: 2023-02-23
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# LightGCL
This is the PyTorch implementation for LightGCL proposed in the paper [**LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation**](https://openreview.net/forum?id=FKXVK9dyMM), *International Conference on Learning Representation*, 2023.
Fig: Model Structure of LightGCL
### 1. Note on datasets and directories
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.
### 2. Running environment
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
```
### 3. How to run the codes
* 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
```
### 4. Some configurable arguments
* `--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.
### 5. Experimental Results
Fig: Experimental Results
Fig: Experimental Results (cont')
*Note: More details for settings can be found in our paper.*
### 6. Citing our paper
```
@inproceedings{caisimple,
title={LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation},
author={Cai, Xuheng and Huang, Chao and Xia, Lianghao and Ren, Xubin},
booktitle={International Conference on Learning Representations (ICLR)},
year={2023}
}
```