Implementation of FedCT framework.

Pivot citation:

@inproceedings{liu2021fedct,
  title={FedCT: Federated Collaborative Transfer for Recommendation},
  author={Liu, Shuchang and Xu, Shuyuan and Yu, Wenhui and Fu, Zuohui and Zhang, Yongfeng and Marian, Amelie},
  booktitle={Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={716--725},
  year={2021}
}

Full code is still under optimization. Please contact Shuchang for temporary code.

0. Setup

conda create -n fedct python=3.9
conda activate fedct
conda install -c anaconda ipykernel
python -m ipykernel install --user --name fedct --display-name "FedCT"
conda install pytorch cudatoolkit=11.3 -c pytorch -c conda-forge 
conda install -c conda-forge scikit-learn
conda install -c conda-forge tqdm 
conda install pandas
conda install setprotitle
pip install matplotlib

1. Domain Environment

1.1 Preprocess

Run notebook 'data/Amazon.ipynb'

Corresponding data will be saved in the specified directory 'XXX/domain_data/' with the following sub contents:

• multicore_data/
• tsv_data/
• meta_data/

Then run:

bash run_domain_reader.sh

This will save data readers in 'XXX/domain_data/reader/'

1.2 Train

bash run_domain_pretrain.sh

Training logs and models will be saved in:

• XXX/env_logs/
• XXX/env_models/

2. Cold-start User Transfer Task

2.1 Preprocess

Run notebook 'data/Amazon_FedCT.ipynb' with your specified target_path (e.g. 'XXX/transfer_data/')

Corresponding data will be saved in the specified directory target_path with the following sub contents:

• id_train.tsv
• domain_model_logs.txt
• ${target_domain}_val.tsv
• ${target_domain}_test.tsv

2.2 Train

bash run_emcdr.sh

bash run_cue.sh

Training logs and models will be saved in:

• XXX/transfer_logs/
• XXX/transfer_models/

3. Result Observation

Notebooks:

• DomainModelResult.ipynb: domain-specific model results, standard recommendation, no cold-start
• ColdStartTransferResult.ipynb: EMCDR and DUE results