user-fairness

This repository includes the implementation for User-oriented Fairness in Recommendation: Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2021. User-oriented Fairness in Recommendation. In Proceedings of the Web Conference 2021 (WWW'21).

Refernece

For inquiries contact Yunqi Li ([email protected]) or Hanxiong Chen ([email protected]) or Yongfeng Zhang ([email protected])

@inproceedings{li2021user,
  title={User-oriented Fairness in Recommendation},
  author={Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang},
  booktitle={Proceedings of the the Web Conference 2021},
  year={2021}
}

Environments

Python 3.6.6

Packages:

pandas==0.24.2
Gurobi==9.0.2

Gurobi is a commercial optimization solver. To run our code, please first install Gurobi and purchase a license. Without a license or fail to install properly, our code will not be able to run.

Datasets

• The processed datasets are in ./dataset/
• Amazon Datasets: The origin dataset can be found here.
• For each dataset directory contains processed splitted testing datasets for re-ranking.
  • 0.05_count_*_test_ratings.txt: grouping by total number of interactions.
  • sum_0.05_price_*_test_ratings.txt: grouping by total consumption.
  • max_0.05_price_*_test_ratings.txt: grouping by maximum price.
• Sample rank files generated by NCF and biasedMF are provided under each dataset directory. Please decompress each .tar.gz file under its directory before running the code.

Run the codes

Prepare input data

• To run the code, please put the ranking file generated by recommendation model under the corresponding dataset folder. For example, to run model with 5Beauty-rand dataset, put the "*_rank.csv" file under "dataset/5Beauty-rand/" directory.
• Ranking csv file format: uid \t iid \t score \t label
  • uid: user id column
  • iid: item id column
  • score: predicted score column
  • label: 0 or 1 to indicate this is a negative sample or positive sample

Modify model.py

• Before running the code, please update the info of "src/model.py" in "__main__" section. You need to update dataset_name, model_name, group_name_title, group_1_file, group_2_file.
  • group_name_title: select from "0.05" (interaction), "max_0.05"(max price) and "sum_0.05"(total consumption)
  • group_1_file: select from "_count_active_test_ratings.txt" (interaction), "_price_active_test_ratings.txt" (max price/total consumption)
  • group_2_file: select from "_count_inactive_test_ratings.txt" (interaction), "_price_inactive_test_ratings.txt" (max price/total consumption)
• You can chage the variable epsilon to control the strictness for fairness.

Run

> cd user-fairness/src/
> python model.py

Result

The result file will be located in ./results/ by default.