LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation
Authors: Xuheng Cai, Chao Huang, Lianghao Xia, Xubin Ren
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on several benchmark datasets demonstrate the significant improvement in performance of our model over the state-of-the-arts. Further analyses demonstrate the superiority of Light GCL s robustness against data sparsity and popularity bias. The source code of our model is available at https://github.com/HKUDS/Light GCL. To verify the superiority and effectiveness of the proposed Light GCL method, we perform extensive experiments to answer the following research questions: |
| Researcher Affiliation | Academia | Xuheng Cai Chao Huang Lianghao Xia Xubin Ren Department of Computer Science, University of Hong Kong {rickcai, lhaoxia}@hku.hk chaohuang75gmail.com xubinrencs@gmail.com |
| Pseudocode | No | The paper describes the model with mathematical equations (e.g., Eq. 1-8) and textual explanations, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code of our model is available at https://github.com/HKUDS/Light GCL. |
| Open Datasets | Yes | We evaluate our model and the baselines on five real-world datasets: Yelp (29,601 users, 24,734 items, 1,517,326 interactions): a dataset collected from the rating interactions on Yelp platform; Gowalla (50,821 users, 57,440 items, 1,172,425 interactions): a dataset containing users check-in records collected from Gowalla platform; ML-10M (69,878 users, 10,195 items, 9,988,816 interactions): a well-known movie-rating dataset for collaborative filtering; Amazon-book (78,578 users, 77,801 items, 2,240,156 interactions): a dataset composed of users ratings on books collected from Amazon; and Tmall (47,939 users, 41,390 items, 2,357,450 interactions): a E-commerce dataset containing users purchase records on different products in Tmall platform. |
| Dataset Splits | Yes | In accordance with He et al. (2020) and Wu et al. (2021), we split the datasets into training, validation and testing sets with a ratio of 7:2:1. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers (e.g., Python version, library versions like PyTorch or TensorFlow) required to reproduce the experiments. |
| Experiment Setup | Yes | the embedding size is set as 32; the batch size is 256; two convolutional layers are used for GCN models. For our Light GCL, the regularization weights λ1 and λ2 are tuned from {1e-5, 1e-6, 1e-7} and {1e4, 1e-5}, respectively. The temperature τ is searched from {0.3, 0.5, 1, 3 ,10}. The dropout rate is chosen from {0, 0.25}. The rank (i.e., q) for SVD, is set as 5. We use the Adam optimizer with a learning rate of 0.001 decaying at the rate of 0.98 until the rate reaches 0.0005. |