Partition-Mallows Model and Its Inference for Rank Aggregation
Partition-Mallows Model and Its Inference for Rank Aggregation
复制标题
划分-Mallows模型及其排序聚合推理
DOI：
10.1080/01621459.2021.1930547
复制
发表时间：
2021
影响因子：
3.7
通讯作者：
Deng Ke
中科院分区：
文献类型：
--
作者：
Zhu Wanchuang;Jiang Yingkai;Liu Jun S.;Deng Ke
学习如何聚合排名列表多年来一直是一个活跃的研究领域，其进展在从生物信息学到互联网商务的许多应用中发挥了至关重要的作用。仅根据排名数据识别排名器的可靠性问题引起了许多从业者的极大兴趣，但却很少受到研究人员的关注。通过将排序后的实体分为两个不相交的组，即相关和不相关/背景的，并结合马洛模型的相关实体的相对排名，我们提出了一个框架的排名聚合，不仅可以区分质量差异的排名，但也提供了详细的排名信息的相关实体。所提出的方法的理论特性的建立，并通过仿真研究和实际数据的应用表明其优于现有的方法。扩展所提出的方法来处理部分排名列表和进行协变量辅助排名聚合进行了讨论。
Learning how to aggregate ranking lists has been an active research area for many years and its advances have played a vital role in many applications ranging from bioinformatics to internet commerce. The problem of discerning reliability of rankers based only on the rank data is of great interest to many practitioners, but has received less attention from researchers. By dividing the ranked entities into two disjoint groups, that is, relevant and irrelevant/background ones, and incorporating the Mallows model for the relative ranking of relevant entities, we propose a framework for rank aggregation that can not only distinguish quality differences among the rankers but also provide the detailed ranking information for relevant entities. Theoretical properties of the proposed approach are established, and its advantages over existing approaches are demonstrated via simulation studies and real-data applications. Extensions of the proposed method to handle partial ranking lists and conduct covariate-assisted rank aggregation are also discussed.