Lagrangian Inference for Ranking Problems
Lagrangian Inference for Ranking Problems
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DOI：
10.1287/opre.2022.2313
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Yue Liu;Ethan X. Fang;Junwei Lu
中科院分区：
文献类型：
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作者：
Yue Liu;Ethan X. Fang;Junwei Lu
了解不同项目的排名顺序在诸如体育、在线游戏和推荐等许多应用中非常重要。本文提供了一种新的方法来推断排名系统。特别是，本文旨在回答这样的问题，是项目A优于项目B？A项是否在前10项中？这样的推理问题是具有挑战性的，因为它们涉及组合结构。关键的技术创新是一个新的拉格朗日推理框架与新的引导工具。强有力的理论保证，显示所提出的方法的最优性。提供了一个使用大规模数据集推断电影排名的新应用来证明所提出的方法的适用性。
Understanding ranking orders of different items is of great importance in many applications such as sports, online game, and recommendation, among many others. This paper provides a novel approach to inferring the ranking systems. In particular, the paper aims to answer questions like, is item A better than item B? Is item A among the top 10 items? Such inference problems are challenging as they involve combinatorial structures. The key technical innovation is a new Lagrangian inference framework with new bootstrap tools. Strong theoretical guarantees are provided showing the optimality of the proposed method. A novel application of inferring movies’ ranking using a large-scale data set is provided to demonstrate the applicability of the proposed method.