  We consider a covariate-assisted ranking model grounded in the Plackett--Luce
framework. Unlike existing works focusing on pure covariates or individual
effects with fixed covariates, our approach integrates individual effects with
dynamic covariates. This added flexibility enhances realistic ranking yet poses
significant challenges for analyzing the associated estimation procedures. This
paper makes an initial attempt to address these challenges. We begin by
discussing the sufficient and necessary condition for the model's
identifiability. We then introduce an efficient alternating maximization
algorithm to compute the maximum likelihood estimator (MLE). Under suitable
assumptions on the topology of comparison graphs and dynamic covariates, we
establish a quantitative uniform consistency result for the MLE with
convergence rates characterized by the asymptotic graph connectivity. The
proposed graph topology assumption holds for several popular random graph
models under optimal leading-order sparsity conditions. A comprehensive
numerical study is conducted to corroborate our theoretical findings and
demonstrate the application of the proposed model to real-world datasets,
including horse racing and tennis competitions.
