This is the code in Contrastive Graph Structure Learning via Information Bottleneck for Recommendation which has been accepted by NeurIPS 2022.

To install requirements:

conda env create -f environment.yaml

To prepare the data for the model training:

python data_process.py

To train the model(s) in the paper:

python train.py

Output: the file "model.tar"

To evaluate my model in the paper:

python evaluate.py