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} }