Abstract

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.

摘要

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，它的进步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。仅基于排名数据来辨别排名的可靠性问题引起了许多从业者的极大兴趣，但很少受到研究人员的关注。通过将排名实体分为两个不相交的组，即相关和不相关/背景实体，并结合相关实体相对排名的 Mallows 模型，我们提出了一个排名聚合框架，该框架不仅可以区分排名者之间的质量差异还提供相关实体的详细排名信息。建立了所提出方法的理论特性，其优于现有方法的优势通过模拟研究和真实数据应用得到证明。还讨论了处理部分排名列表和进行协变量辅助排名聚合的拟议方法的扩展。