2021/01/01 by Wanchuang Zhu, Yingkai Jiang, Zhu, Wanchuang +5 · 1 citation
Computer Science · Economics, Econometrics and Finance · Mathematics · #Aggregate (composite) #Artificial intelligence #Bayesian Modeling and Causal Inference #Census and Population Estimation #Computer science #Covariate #Data mining #Disjoint sets #Game Theory and Voting Systems #Inference #Information retrieval #Learning to rank #Machine learning #Mathematics #Partition (number theory) #Rank (graph theory) #Ranking (information retrieval) #stat.ME
paper · pdf · doi:10.48550/arxiv.2104.07261
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2021/01/01 · arxiv created 2021/04/15 · arxiv updated 2021/04/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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, i.e., 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.