jianzhu / SGL

SGL

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.

Environment Requirement

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

Quick Start

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

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 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):

yelp2018 dataset

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

amazon-book dataset

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

ifashion dataset

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 --recommender=SGL