  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.
