Contrastive Graph Structure Learning via Information Bottleneck for Recommendation

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

Requirements

To install requirements:

conda env create -f environment.yaml

Data Process

To prepare the data for the model training:

python data_process.py

Training

To train the model(s) in the paper:

python train.py

Output: the file "model.tar"

Evaluation

To evaluate my model in the paper:

python evaluate.py