Data Science Seminar
Optimizing Ranking Effectiveness and Fairness
Tao Yang
Optimizing Ranking Effectiveness and Fairness
| When | Wednesday, December 7, 2022, 10:30 AM – 11:45 AM (MT) |
|---|---|
| Where | WEB 3780 |
Abstract
Advanced ranking techniques have led to improvements in AI-powered information services that significantly changed people’s lives. For example, search engines that rank information according to their utilities to use’s queries have helped billions of people better finish their tasks in daily work; recommendation systems that rank products/movies/news according to the user’s interests have completely changed the way people discover information everyday. Therefore, how to construct and optimize ranking systems is one of the most important research problems in the field of Information Retrieval (IR). When optimizing ranking systems, there are two important criteria to measure the quality of result rankings in IR systems. The first criterion is ranking effectiveness, which refers to the ability of a ranking system to effectively present results based on their relevance to the users’ needs. The second criterion is ranking fairness, which refers to the ability of a ranking system to present results fairly. For example, in job recommendation, if a ranking system only considers ranking effectiveness and ranks items solely according to relevance, a small number of top candidates will always be exposed to users and dominate users’ attention as users usually only examine the top ranks. In such case, other candidates will rarely have the chance to be hired even when they are highly qualified for the job. Therefore, it is important to balance the effectiveness of ranked lists with the fairness in ranking optimization. In this talk, I will present my recent works on ranking effectiveness and fairness optimization. The talk will be two parts. In the first part of this talk, I will introduce works on sole-effectiveness optimization where I propose uncertainty-aware rank systems based on Bayes modelling. In the second part of this talk, I will introduce works on fairness-effectiveness joint optimization.
Speaker
Tao Yang
Utah SoC
Tao Yang is fourth year Ph.D. student from the University of Utah, supervised by Prof. Qingyao Ai and Prof. Jeff M Phillips. He mainly focuses on Information Retrieval (IR) and Machine Learning related topics. Especially, he mainly focuses on how to construct and optimize ranking systems while considering ranking effectiveness and fairness. His works have been published on top-tier IR conferences and journals, like SIGIR, WWW, CIKM,WSDM,TOIS,….
Tags: fairness & ethics optimization
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