[ICLR'2023]“LightGCL：用于推荐的简单而有效的图对比学习”_Python_下载.zip

# 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. <br> <p align='center'> <img src="https://user-images.githubusercontent.com/60952950/219573564-64d5e9cc-6dbc-4cc9-b115-95fb6d46f1a7.png" width="600" height="300"><br> <i> Fig: Model Structure of LightGCL </i> </p> ### 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 0 ``` * ML-10M ``` python main.py --data ml10m --temp 0.5 ``` * Tmall ``` python main.py --data tmall --gnn_layer 1 ``` * Amazon ``` python main.py --data amazon --gnn_layer 1 --lambda2 0 --temp 0.1 ``` ### 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. On the complexity of LightGCL We notice that many readers are confused about the complexity of performing graph convolution on the SVD-reconstructed view, arguing that the complexity should be O(2IJLd) since the SVD-reconstructed view is fully-connected. In fact, this issue has been clearly explained in the **Appendix D.3** in our paper. We also answered a Github issue about it (<a href='https://github.com/HKUDS/LightGCL/issues/3'>issue #3</a>). We hereby clarify again: It is correct that the reconstructed graph is fully connected. However, please note that the reconstructed graph is actually the **product of three low dimension matrices U,S,V'**, whose dimensions are I×q, q×q, q×J, respectively (where q is as small as 5). So we don't really need to compute the reconstructed graph, but just store the three low-dimension matrices. And by doing the matrix multiplication in the following order: US [pre-calculated, complexity not counted into training], V'E [complexity is O(qJd)], and then (US)(V'E) [complexity is O(qId)], we never need to construct that large matrix, and the complexity is proportional to **(I+J)** instead of **(IJ)**. ### 6. Citing our paper Please kindly cite our paper if you find this paper and the codes helpful. ``` @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={The Eleventh International Conference on Learning Representations}, year={2023} } ```