This is our Tensorflow implementation for our SIGIR 2021 paper:
Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian,and Xing Xie. 2021. Self-supervised Graph Learning for Recommendation, Paper in arXiv.
The code runs well under python 3.7.7. The required packages are as follows:
- Tensorflow-gpu == 1.15.0
- numpy == 1.19.1
- scipy == 1.5.2
- pandas == 1.1.1
- cython == 0.29.21
Firstly, compline the evaluator of cpp implementation with the following command line:
python setup.py build_ext --inplaceIf the compilation is successful, the evaluator of cpp implementation will be called automatically. Otherwise, the evaluator of python implementation will be called.
Note that the cpp implementation is much faster than python.
Further details, please refer to NeuRec
Secondly, specify dataset and recommender in configuration file NeuRec.properties.
Model specific hyperparameters are in configuration file ./conf/SGL.properties.
Some important hyperparameters (taking a 3-layer SGL-ED as example):
aug_type=1
reg=1e-4
embed_size=64
n_layers=3
ssl_reg=0.1
ssl_ratio=0.1
ssl_temp=0.2
aug_type=1
reg=1e-4
embed_size=64
n_layers=3
ssl_reg=0.5
ssl_ratio=0.1
ssl_temp=0.2
aug_type=1
reg=1e-3
embed_size=64
n_layers=3
ssl_reg=0.02
ssl_ratio=0.4
ssl_temp=0.5
Finally, run main.py in IDE or with command line:
python main.py