This is the PyTorch implementation for our SIGIR 2021 paper. We also provide Tensorflow implementation for SGL: https://github.com/wujcan/SGL-TensorFlow.

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.

This project is based on NeuRec. Thanks to the contributors.

The code runs well under python 3.7.7. The required packages are as follows:

• pytorch == 1.9.1
• numpy == 1.20.3
• scipy == 1.7.1
• pandas == 1.3.4
• cython == 0.29.24

Firstly, compline the evaluator of cpp implementation with the following command line:

python local_compile_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 that the cpp implementation is much faster than python.

Secondly, change the value of variable root_dir and data_dir in main.py, then specify dataset and recommender in configuration file NeuRec.ini.

Model specific hyperparameters are in configuration file ./conf/SGL.ini.

Some important hyperparameters (taking a 3-layer SGL-ED as example):

aug_type=ED
reg=1e-4
embed_size=64
n_layers=3
ssl_reg=0.1
ssl_ratio=0.1
ssl_temp=0.2

aug_type=ED
reg=1e-4
embed_size=64
n_layers=3
ssl_reg=0.5
ssl_ratio=0.1
ssl_temp=0.2

aug_type=ED
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 --recommender=SGL --dataset=yelp2018 --aug_type=ED --reg=1e-4 --n_layers=3 --ssl_reg=0.1 --ssl_ratio=0.1 --ssl_temp=0.2

python main.py --recommender=SGL --dataset=amazon-book --aug_type=ED --reg=1e-4 --n_layers=3 --ssl_reg=0.5 --ssl_ratio=0.1 --ssl_temp=0.2

python main.py --recommender=SGL --dataset=ifashion --aug_type=ED --reg=1e-3 --n_layers=3 --ssl_reg=0.02 --ssl_ratio=0.4 --ssl_temp=0.5