PPAC

This is the source code of PPAC.

Paper: Debiasing Recommendation with Popular Popularity (WWW'24)

Running environment

conda create -n ppac python=3.8.8
conda activate ppac

pip install torch==1.13.1 --index-url https://download.pytorch.org/whl/cu116
pip install gym==0.23.0 tensorflow-probability==0.20.1 matplotlib scikit-learn
pip install dgl==1.1.3 -f https://data.dgl.ai/wheels/cu116/repo.html

Guidelines to run our codes

If you want to train PPAC, when you use mf/ncf as base model, please use the following scripts:

python run_MF.py --model ppacmf --dataset {dataset} --train
python run_MF.py --model ppacncf --dataset {dataset} --train

If you want to use LightGCN as base model and train PPAC:

python run_LightGCN.py --model ppaclg --dataset {dataset} --train

${dataset} can be chosen from ['ml-1M', 'gowalla', 'yelp2018'].

For example:

python run_MF.py --model ppacmf --dataset ml-1M --train

After training, if you want to use your pre-trained model to conduct inference, use the below script (remove --train flag).

python run_MF.py --model ppacmf --dataset {dataset} --gamma {gamma} --beta {beta}
python run_MF.py --model ppacncf --dataset {dataset} --gamma {gamma} --beta {beta}
python run_LightGCN.py --model ppaclg --dataset {dataset} --gamma {gamma} --beta {beta}

We provide the hyper-parameters used in our experiments for your references.

	ml-1M		Gowalla		Yelp2018
	$\gamma$	$\beta$	$\gamma$	$\beta$	$\gamma$	$\beta$
BPRMF	64	-32	512	-1024	256	-512
NCF	32	-16	64	-256	128	-256
LightGCN	16	-8	64	-512	32	-128

For example:

python run_MF.py --model ppacmf --dataset ml-1M --gamma 64 --beta -32

Tips to tune $\gamma$ and $\beta$

Since $\gamma$ and $\beta$ are only used during inference, we can set both values to zero during training. Once the results on validation sets converge and the training process is completed, we begin tuning the values of $\gamma$ and $\beta$ on the validation sets. Since the model learnable parameters are fixed, we only need to conduct inference, which is very fast (only several seconds).

Citation

If you use our datasets or codes, please cite our paper.

@inproceedings{PPAC,
    author = {Ning, Wentao and Cheng, Reynold and Yan, Xiao and Kao, Ben and Huo, Nan and Haldar, Nur Al Hasan and Tang, Bo},
    title = {Debiasing Recommendation with Popular Popularity},
    booktitle = {WWW},
    publisher = {ACM},
    year = {2024}
}