α-Rank: Unified Item-Fair Ranking from A Cooperative Game Theory View

16 Sept 2023 (modified: 25 Mar 2024)ICLR 2024 Conference Withdrawn SubmissionEveryoneRevisionsBibTeX
Keywords: item fairness, cooperative game theory, ranking
TL;DR: We introduced the α-rank framework, utilizing Optimal Transport (OT), to optimize a smooth fairness objective for effectively balancing various item fairness concepts.
Abstract: Driven by economic and systematic considerations, the pursuit of item fairness in ranking has emerged as a prominent topic in recommendation and advertising applications. Prior research has suggested various fairness aspects can be aligned with the concept of distributive justice in sociology, such as utilitarianism, dealism, and egalitarianism. However, they fail to distinguish the distinctions and relationships among these fairness dimensions in ranking. In fact, item fairness can be viewed as a unified challenge of fairly allocating the constrained and fluctuating resources, from the perspective of cooperative game theory. In our work, we introduce the smooth α-fairness objective for different fairness and unify item fairness as a cooperative game problem. In such games, items are considered as the players dividing the cake of user attention. In such games, we analyze the α-fairness objective from a theoretical way and introduce an efficient approach called α-rank. Firstly, we re-form several important axioms in cooperative games to tell us how item fairness principles exhibit when the resource ``cake'' changes in ranking. Then we designed α-rank, which applies the optimal transport to conduct item fairness. Theoretical analysis provides an upper bound, showcasing the maximum total utility loss across different fairness degrees. we conducted experiments in two ranking applications: recommendation and advertising. The experimental results demonstrate that α-rank effectively and efficiently outperforms the baseline methods.
Supplementary Material: zip
Primary Area: societal considerations including fairness, safety, privacy
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2024/AuthorGuide.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Submission Number: 657
Loading