AdvInfoNCE

Overview

Code of "Empowering Collaborative Filtering Generalization via Principled Adversarial Contrastive Loss"

Run the Code

We provide implementation for various baselines presented in the paper.
To run the code, first run the following command to install tools used in evaluation:

python setup.py build_ext --inplace

LightGCN backbone

AdvInfoNCE Training:

Tencent:

python train_AdvInfoNCE.py --train_norm --pred_norm --modeltype AdvInfoNCE --model_version embed --dataset tencent_synthetic --n_layers 2 --lr 1e-3 --batch_size 2048 --neg_sample 128 --tau 0.09 --adv_lr 5e-5 --eta_epochs 7 --k_neg 64 --patience 20 --dsc sota_tencent_lgn

KuaiRec:

python train_AdvInfoNCE.py --train_norm --pred_norm --modeltype AdvInfoNCE --model_version embed  --dataset kuairec2 --n_layers 2 --neg_sample 128 --tau 2 --lr 3e-5 --batch_size 2048 --adv_lr 5e-5 --eta_epochs 12 --warm_up_epochs 0 --adv_interval 5 --dsc sota_kuairec_lgn

Yahoo:

python train_AdvInfoNCE.py --train_norm --pred_norm --model_version embed --modeltype AdvInfoNCE --dataset yahoo.new --n_layers 2 --neg_sample 64 --tau 0.28 --lr 5e-4 --batch_size 1024 --adv_lr 1e-4 --eta_epochs 13 --dsc sota_yahoo_lgn

Coat:

python train_AdvInfoNCE.py --train_norm --pred_norm --modeltype AdvInfoNCE --model_version embed --dataset coat --n_layers 2 --neg_sample 64 --tau 0.75 --lr 1e-3 --batch_size 1024 --adv_lr 1e-2 --eta_epochs 20 --adv_interval 15 --dsc sota_coat_lgn

INFONCE Training:

python main.py --train_norm --pred_norm --modeltype  INFONCE --dataset kuairec2 --n_layers 2 --batch_size 2048 --lr 3e-5 --neg_sample 128 --tau 2  --dsc infonce

MF backbone

AdvInfoNCE Training:

Tencent:

python train_AdvInfoNCE.py --train_norm --pred_norm --modeltype AdvInfoNCE --model_version embed --dataset tencent_synthetic --n_layers 0 --lr 1e-3 --batch_size 2048 --neg_sample 128 --tau 0.09 --adv_lr 5e-5 --eta_epochs 8 --patience 20 --dsc sota_tencent

Yahoo:

python train_AdvInfoNCE.py --train_norm --pred_norm --model_version embed --modeltype AdvInfoNCE --dataset yahoo.new --n_layers 0 --neg_sample 64 --tau 0.28 --lr 5e-4 --batch_size 1024 --adv_lr 1e-4 --eta_epochs 12 --dsc sota_yahoo

Coat:

python train_AdvInfoNCE.py --train_norm --pred_norm --modeltype AdvInfoNCE --model_version embed --dataset coat --n_layers 0 --neg_sample 64 --tau 0.75 --lr 1e-3 --batch_size 1024 --adv_lr 1e-2 --eta_epochs 18 --adv_interval 15 --dsc sota_coat

InfoNCE Training:

python main.py --train_norm --pred_norm --modeltype INFONCE --dataset tencent_synthetic --n_layers 0 --tau 0.09 --neg_sample 128 --batch_size 2048 --lr 1e-3 --dsc infonce

Requirements

python == 3.7.10
pytorch == 1.12.1+cu102
tensorflow == 1.14
reckit == 0.2.4

Reference

If you want to use our codes and datasets in your research, please cite:

@inproceedings{AdvInfoNCE,
  title={Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss},
  author={Zhang, An and Sheng, Leheng and Cai, Zhibo and Wang, Xiang and Chua, Tat-Seng},
  booktitle={{NeurIPS}},
  year={2023}
}

Samwei1 / AdvInfoNCE