IJCAI 2022poster78 citations
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness (Extended Abstract)
Abstract
Computing the gradient of stochastic Plackett-Luce (PL) ranking models for relevance and fairness metrics can be infeasible because it requires iterating over all possible permutations of items. In this paper, we introduce a novel algorithm: PL-Rank, that estimates the gradient of a PL ranking model through sampling. Unlike existing approaches, PL-Rank makes use of the specific structure of PL models and ranking metrics. Our experimental analysis shows that PL-Rank has a greater sample-efficiency and is computationally less costly than existing policy gradients, resulting in faster convergence at higher performance.
Artificial Intelligence: General
BibTeX
@inproceedings{ijcai2022p743,
title = {Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness (Extended Abstract)},
author = {Oosterhuis, Harrie},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {5319--5323},
year = {2022},
month = {7},
note = {Sister Conferences Best Papers},
doi = {10.24963/ijcai.2022/743},
url = {https://doi.org/10.24963/ijcai.2022/743},
}