Dynamic Causal Collaborative Filtering

Introduction

This repository includes the implementation for Dynamic Causal Collaborative Filtering

Paper: Dynamic Causal Collaborative Filtering
Paper Link: https://dl.acm.org/doi/abs/10.1145/3511808.3557300

Environment

Environment requirements can be found in ./requirement.txt

Datasets

• Electronics: The origin dataset can be found here.

• MovieLens-1M: The origin dataset can be found here.

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

Example to run the codes

For example:

# DyCCF on Electronics dataset based on GRU4Rec model
> cd ./src/
> python main-3phases.py --model GRU4Rec --dataset Electronics-3 --epoch 100 --phase1 0 --batch_size 256 --dccf 1 --eval_batch_size 5000 --gpu 3 --ctf_num 3 --load 1 --train 0 --metrics nDCG@10,hit@10,unbiasedndcg@10,unbiasedhit@10

Citation

@inproceedings{xu2022dynamic,
  title={Dynamic causal collaborative filtering},
  author={Xu, Shuyuan and Tan, Juntao and Fu, Zuohui and Ji, Jianchao and Heinecke, Shelby and Zhang, Yongfeng},
  booktitle={Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
  pages={2301--2310},
  year={2022}
}