This is the Pytorch implementation for our paper "Propagation then Distillation: Understanding and Improving Linear GCNs for Recommendation"
pip install -r requirements.txt
We provide four processed datasets: Gowalla, Yelp2018, Home&Kitchen and Amazon-CD.
Gowalla:
python -u train.py --dataset gowalla --drop_ratio 0.1 --t 0.06 --a 0 --norm_type 0.9 --beta 0.4
Yelp2018:
python -u train.py --dataset yelp2018 --drop_ratio 0.1 --t 0.11 --a 20 --norm_type 0.6 --beta 1
Home&Kitchen:
python -u train.py --dataset homekitchen --drop_ratio 0.2 --t 0.11 --a 30 --norm_type 1 --beta 1
Amazon_CD:
python -u train.py --dataset amazon-cd --drop_ratio 0.2 --t 0.13 --a 30 --norm_type 0.45 --beta 0.1