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Deconfounded Causal Collaborative Filtering

Introduction

This repository includes the implementation for Deconfounded Causal Collaborative Filtering

Paper: Deconfounded Causal Collaborative Filtering
Paper Link: https://dl.acm.org/doi/full/10.1145/3606035

Environment

Environment requirements can be found in ./requirement.txt

Datasets

  • Electronics and CDs and Vinyl: The origin dataset can be found here.

  • Yelp: The origin dataset can be found here.

  • The data processing code can be found in ./src/data_preprocessing/

Example to run the codes

For example:

# DCCF on Electronics dataset
> cd ./src/
> python ./main.py --rank 1 --model_name DCCF --optimizer Adam --lr 0.001 --dataset Electronics --metric ndcg@5,recall@5,precision@5 --gpu 0 --epoch 100 --test_neg_n 1000

Citation

@article{xu2023deconfounded,
  title={Deconfounded causal collaborative filtering},
  author={Xu, Shuyuan and Tan, Juntao and Heinecke, Shelby and Li, Vena Jia and Zhang, Yongfeng},
  journal={ACM Transactions on Recommender Systems},
  volume={1},
  number={4},
  pages={1--25},
  year={2023},
  publisher={ACM New York, NY}
}

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