An Extended Mallows Model for Ranked Data Aggregation
Publication in refereed journal


摘要In this article, we study the rank aggregation problem, which aims to find a consensus ranking by aggregating multiple ranking lists. To address the problem probabilistically, we formulate an elaborate ranking model for full and partial rankings by generalizing the Mallows model. Our model assumes that the ranked data are generated through a multistage ranking process that is explicitly governed by parameters that measure the overall quality and stability of the process. The new model is quite flexible and has a closed form expression. Under mild conditions, we can derive a few useful theoretical properties of the model. Furthermore, we propose an efficient statistic called rank coefficient to detect over-correlated rankings and a hierarchical ranking model to fit the data. Through extensive simulation studies and real applications, we evaluate the merits of our models and demonstrate that they outperform the state-of-the-art methods in diverse scenarios. Supplementary materials for this article are available online.
著者Han Li, Minxuan Xu, Jun S. Liu, Xiaodan Fan
期刊名稱Journal of the American Statistical Association
出版社Taylor & Francis
頁次730 - 746
關鍵詞Consensus ranking, Kendall tau distance, Rank aggregation, Rank coefficient

上次更新時間 2020-23-11 於 01:56