Presents a constraint-satisfaction framework to jointly infer linear scoring parameters and latent group bonuses from observed rankings, with NP-hardness in general and polynomial-time solvability for constant feature dimension and group count, plus experiments on real and synthetic data.
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Explaining Rankings with Hidden Group Bonuses
Presents a constraint-satisfaction framework to jointly infer linear scoring parameters and latent group bonuses from observed rankings, with NP-hardness in general and polynomial-time solvability for constant feature dimension and group count, plus experiments on real and synthetic data.