This is our Tensorflow implementation for our EGCL 2024 paper and a part of baselines:
Bin Wu, Bo Zhang, Yihao Tian, Jing Liang & Yangdong Ye. EGCL: An Effective and Efficient Graph Contrastive Learning Framework for Social Recommendation, IEEE Transactions on Cybernetics, Submission 2024.
The code has been tested running under Python 3.6.5. The required packages are as follows:
- tensorflow == 1.14.0
- numpy == 1.16.4
- scipy == 1.3.1
- pandas == 0.17
We have implemented C++ code to output metrics during and after training, which is much more efficient than python evaluator. It needs to be compiled first using the following command.
python setup.py build_ext --inplace
If the compilation is successful, the evaluator of cpp implementation will be called automatically. Otherwise, the evaluator of python implementation will be called. NOTE: The cpp implementation is much faster than python.**
run main.py in IDE or with command line:
python main.py
NOTE :
(1) the duration of training and testing depends on the running environment.
(2) set model hyperparameters on .\conf\BPRMF.properties
(3) set NeuRec parameters on .\NeuRec.properties
(4) the log file save at .\log\Ciao\
We provide Ciao dataset.
- .\dataset\Ciao.rating and Ciao.uu
- Each line is a user with her/his positive interactions with items: userID \ itemID \ ratings.
- Each user has more than 0 associated actions.
The list of available models in BPRMF along with their paper citations, are shown below:
| General Recommender | Paper |
|---|---|
| BPRMF | Steffen Rendle et al. BPR: Bayesian Personalized Ranking from Implicit Feedback. UAI 2009. |
| LightGCN | Xiangnan He, et al. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. SIGIR 2020. |