This is an implementation for our WWW 2022 paper Learning Robust Recommenders through Cross-Model Agreement.
- torch == 1.9.0+cu102
- Numpy
- python3
- scipy
- pandas
-
Movielens-100k: We provide the link to the original data and also the processed dataset in the folder
data
. -
Modcloth and Electronics: These two datasets were first processed by the paper
Addressing Marketing Bias in Product Recommendations
. Then we converted them into our format. If you need to use this dataset, you may also need to cite this paper. -
Adressa: This dataset is from
DenoisingRec
, and it's already our format. If you use this dataset, you may also need to cite the paperDenoising Implicit Feedback for Recommendation.
.
Key parameters are all provided in the file configs.py
, and you can let the code choose the specific parameters for the model and the dataset with "python xxx.py --default".
We provide following commands for our methods DeCA
and DeCA(p)
.
Simply run the code below will return the results shown in the paper:
python main.py --model GMF --dataset ml-100k --method DeCA --default
where --default
means using the default setting. --model
is the model drawn from GMF, NeuMF, CDAE, LightGCN
, --dataset
should be in ml-100k, modcloth, adressa, electronics
, --method
need to be in DeCA, DeCAp
. Remove the --method
term, the code will run normal training.
If you want to use your own settings, try:
python main.py --model GMF --dataset modcloth --C_1 1000 --C_2 10 --alpha 0.5 --method DeCA
If you use our codes in your research, please cite our paper.