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Many properties in the real world doesn't have metrics and can't benumerically observed, making them difficult to learn. To deal with thischallenging problem, prior works have primarily focused on estimating thoseproperties by using graded human scores as the target label in the training.Meanwhile, rating algorithms based on the Bradley-Terry model are extensivelystudied to evaluate the competitiveness of players based on their matchhistory. In this paper, we introduce the Neural Bradley-Terry Rating (NBTR), anovel machine learning framework designed to quantify and evaluate propertiesof unknown items. Our method seamlessly integrates the Bradley-Terry model intothe neural network structure. Moreover, we generalize this architecture furtherto asymmetric environments with unfairness, a condition more commonlyencountered in real-world settings. Through experimental analysis, wedemonstrate that NBTR successfully learns to quantify and estimate desiredproperties.

Neural Bradley-Terry Rating: Quantifying Properties from Comparisons