Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness

This repository contains the code used for the experiments in "Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness" published at SIGIR 2021 (preprint available).

Citation

If you use this code to produce results for your scientific publication, or if you share a copy or fork, please refer to our SIGIR 2021 paper:

@inproceedings{oosterhuis2021plrank,
  Author = {Oosterhuis, Harrie},
  Booktitle = {Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR`21)},
  Organization = {ACM},
  Title = {Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness},
  Year = {2021}
}

License

The contents of this repository are licensed under the MIT license. If you modify its contents in any way, please link back to this repository.

Usage

This code makes use of Python 3, the numpy and the tensorflow packages, make sure they are installed.

A file is required that explains the location and details of the LTR datasets available on the system, for the Yahoo! Webscope, MSLR-Web30k, and Istella datasets an example file is available. Copy the file:

cp example_datasets_info.txt local_dataset_info.txt

Open this copy and edit the paths to the folders where the train/test/vali files are placed.

Here are some command-line examples that illustrate how the results in the paper can be replicated. First create a folder to store the resulting models:

mkdir local_output

To optimize NDCG use run.py with the --loss flag to indicate the loss to use (PL_rank_1/PL_rank_2/lambdaloss/pairwise/policygradient/placementpolicygradient); --cutoff indicates the top-k that is being optimized, e.g. 5 for [email protected]; --num_samples the number of samples to use per gradient estimation (with dynamic for the dynamic strategy); --dataset indicates the dataset name, e.g. Webscope_C14_Set1. The following command optimizes [email protected] with PL-Rank-2 and the dynamic sampling strategy on the Yahoo! dataset:

python3 run.py local_output/yahoo_ndcg5_dynamic_plrank2.txt --num_samples dynamic --loss PL_rank_2 --cutoff 5 --dataset Webscope_C14_Set1

To optimize the disparity metric for exposure fairness use fairrun.py this has the additional flag --num_exposure_samples for the number of samples to use to estimate exposure (this must always be a greater number than --num_samples). The following command optimizes disparity with PL-Rank-2 and the dynamic sampling strategy on the Yahoo! dataset with 1000 samples for estimating exposure:

python3 fairrun.py local_output/yahoo_fairness_dynamic_plrank2.txt --num_samples dynamic --loss PL_rank_2 --cutoff 5 --num_exposure_samples 1000 --dataset Webscope_C14_Set1