A Taxation Perspective for Fair Re-ranking - SIGIR 2024
Offered By: Association for Computing Machinery (ACM) via YouTube
Course Description
Overview
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.
Syllabus
SIGIR 2024 T3.1 [fp] A Taxation Perspective for Fair Re-ranking
Taught by
Association for Computing Machinery (ACM)
Related Courses
Learning From Ranks, Learning to Rank - Jean-Philippe Vert, Google BrainAlan Turing Institute via YouTube Fully Online Matching II - Beating Ranking and Water-filling
IEEE via YouTube NLP4L - Using Corpus and Learning-to-Rank for Better Search Results
BasisTech via YouTube Les coulisses des systèmes de recommandation
Université de Montréal via edX ACM ICTIR 2024 - Theoretical Aspects of Information Retrieval
Association for Computing Machinery (ACM) via YouTube