为什么不:修改对个人不公平的前k排名

Zixuan Chen, P. Manolios, Mirek Riedewald
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引用次数: 0

摘要

这项工作考虑了top-k查询和基于分数的排名函数上下文中的why-not问题。在多目标优化中,我们遵循流行的线性标量化方法,基于多个分数的加权和来研究排名。给定的权重选择可能会引起争议,或者被认为对某些个人或组织不公平,从而引发以下问题:为什么某些利益实体尚未出现在前k名中?我们引入了各种关于why-not-yet查询的概念,并将其正式定义为可满足性或优化问题,其目标是提出解决感兴趣实体位置的替代排序函数。虽然有些“为什么还没有”问题具有线性约束,但其他问题则需要量词、析取和否定。我们提出了几种优化方法,从单调核心结构(用线性约束的结合近似复杂约束)到各种技术(让用户控制运行时间和近似质量之间的权衡)。用真实数据和合成数据进行的实验证明了我们技术的实用性和可伸缩性,显示了它与现有技术(SOA)相比的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Why Not Yet: Fixing a Top-k Ranking that Is Not Fair to Individuals
This work considers why-not questions in the context of top-k queries and score-based ranking functions. Following the popular linear scalarization approach for multi-objective optimization, we study rankings based on the weighted sum of multiple scores. A given weight choice may be controversial or perceived as unfair to certain individuals or organizations, triggering the question why some entity of interest has not yet shown up in the top-k. We introduce various notions of such why-not-yet queries and formally define them as satisfiability or optimization problems, whose goal is to propose alternative ranking functions that address the placement of the entities of interest. While some why-not-yet problems have linear constraints, others require quantifiers, disjunction, and negation. We propose several optimizations, ranging from a monotonic-core construction that approximates the complex constraints with a conjunction of linear ones, to various techniques that let the user control the tradeoff between running time and approximation quality. Experiments with real and synthetic data demonstrate the practicality and scalability of our technique, showing its superiority compared to the state of the art (SOA).
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