A Taxation Perspective for Fair Re-ranking - SIGIR 2024

Explore a novel approach to fair re-ranking in information retrieval systems through a taxation perspective. Delve into the research presented by authors Chen Xu, Xiaopeng Ye, Wenjie Wang, Liang Pang, Jun Xu, and Tat-Seng Chua in this 14-minute conference talk from the Association for Computing Machinery (ACM). Learn how the concept of taxation can be applied to address fairness issues in ranking algorithms, potentially improving equity in search results and recommendations. Gain insights into the methodology, findings, and implications of this innovative study, which aims to enhance the fairness of information retrieval systems while maintaining their effectiveness.