论文标题
频谱排名与协变量
Spectral Ranking with Covariates
论文作者
论文摘要
我们考虑了鉴于他们的不完整和嘈杂的成对比较，对n个玩家进行排名的问题的方法，但是根据播放器协变量信息，我们重新审视了这个经典问题。我们提出了三种频谱排名方法，它们结合了播放器协变量，并分别基于序列化，低级别的结构假设和规范相关性。对合成和现实世界数据集的广泛数值模拟表明，我们提出的方法与现有的基于协方差的排名算法相比有利。
We consider spectral approaches to the problem of ranking n players given their incomplete and noisy pairwise comparisons, but revisit this classical problem in light of player covariate information. We propose three spectral ranking methods that incorporate player covariates and are based on seriation, low-rank structure assumption and canonical correlation, respectively. Extensive numerical simulations on both synthetic and real-world data sets demonstrated that our proposed methods compare favorably to existing state-of-the-art covariate-based ranking algorithms.
