Introducing the Expohedron for Efficient Pareto-optimal Fairness-Utility Amortizations in Repeated Rankings.
Till Kletti|Jean-Michel Renders|Patrick Loiseau
| Anthology ID: | DBLP:conf/wsdm/KlettiRL22 |
|---|---|
| Volume: | WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21 - 25, 2022 |
| Year: | 2022 |
| Venue: | Web Search and Data Mining (WSDM) |
| Publisher: | ACM |
| Pages: | 498-507 |
| URL: | https://doi.org/10.1145/3488560.3498490 |
| DOI: | https://doi.org/10.1145/3488560.3498490 |
| DBLP: | conf/wsdm/KlettiRL22 |
| BibTeX: |
@inproceedings{kletti-2022-introducing,
author = {Till Kletti and
Jean-Michel Renders and
Patrick Loiseau},
editor = {K. Sel\c{c}uk Candan and
Huan Liu and
Leman Akoglu and
Xin Dong and
Jiliang Tang},
title = {{Introducing the Expohedron for Efficient Pareto-optimal Fairness-Utility Amortizations in Repeated Rankings}},
booktitle = {{WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21 - 25, 2022}},
pages = {498--507},
publisher = {ACM},
year = {2022},
url = {https://doi.org/10.1145/3488560.3498490},
doi = {https://doi.org/10.1145/3488560.3498490}
}