Spectral Ranking with Covariates
This work addresses ranking challenges in competitive or comparative settings where additional player information is available, representing an incremental improvement over existing covariate-based methods.
The authors tackled the problem of ranking players from noisy pairwise comparisons by incorporating player covariates, proposing three spectral methods. Their methods showed favorable performance compared to existing state-of-the-art covariate-based ranking algorithms in simulations on synthetic and real-world datasets.
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.