td-vae-cf's Introduction

TD-VAE-CF

This repo covers the implementation for our paper:

Zhaolin Gao, Tianshu Shen, Zheda Mai, Mohamed Reda Bouadjenek, Isaac Waller, Ashton Anderson, Ron Bodkin, and Scott Sanner. "Mitigating the Filter Bubble while Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems" In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 22).

Instructions

Download dataset from https://drive.google.com/drive/folders/1o1izS1Mjptmq8SG5lodc2F6guiifxna7?usp=sharing.
Modify the api_key in line 96 of main.py to your api key on comet_ml.
Train and evaluate:

python main.py --data_name yelp_SIGIR --target veg_bbq --lamb LAMB_VALUE --std STD_VALUE
python main.py --data_name yelp_SIGIR --target fried_salad --lamb LAMB_VALUE --std STD_VALUE
python main.py --data_name reddit --target men_women --lamb LAMB_VALUE --std STD_VALUE
python main.py --data_name reddit --target rep_dem --lamb LAMB_VALUE --std STD_VALUE

Citation

If you find this code useful in your research, please cite the following paper:

@inproceedings{gao2022sigir,
  title={Mitigating the Filter Bubble while Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems},
  author={Zhaolin Gao, Tianshu Shen, Zheda Mai, Mohamed Reda Bouadjenek, Isaac Waller, Ashton Anderson, Ron Bodkin, Scott Sanner},
  booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year={2022}
}