TTEN

This is the official implementation of Test Time Embedding Normalization for Popularity Bias Mitigation, CIKM 2023.

Requirements

• python == 3.9.12
• pytorch == 1.13.0
• scipy == 1.11.1
• numpy == 1.25.2
• pandas == 2.0.3
• tqdm == 4.66.1
• scikit-learn == 1.3.0

Clone the repository and install requirements with

conda create -n TTEN python=3.9.12
conda activate TTEN

pip install -r requirements.txt

Run the Code

Gowalla

python main.py --loss_type ssm --lr 0.001 --ssm_temp 0.1 --dataset fair_gowalla --tten

Yelp2018

python main.py --loss_type ssm --lr 0.001 --ssm_temp 0.12 --dataset fair_yelp2018 --tten

ML10M

python main.py --loss_type ssm --lr 0.001 --ssm_temp 0.1 --dataset fair_ml10m --tten

Citation

@inproceedings{kim2023test,
  title={Test-Time Embedding Normalization for Popularity Bias Mitigation},
  author={Kim, Dain and Park, Jinhyeok and Kim, Dongwoo},
  booktitle={Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
  pages={4023--4027},
  year={2023}
}