Many properties in the real world can't be directly observed, making them difficult to learn. To deal with this challenging problem, prior works have primarily focused on estimating those properties by using graded human scores as the target label in the training. Meanwhile, rating algorithms based on the Bradley-Terry model are extensively studied to evaluate the competitiveness of players based on their match history. In this paper, we introduce the Deep Bradley-Terry Rating (DBTR), a novel machine learning framework designed to quantify and evaluate properties of unknown items. Our method seamlessly integrates the Bradley-Terry model into the neural network structure. Moreover, we generalize this architecture further to asymmetric environments with unfairness, a condition more commonly encountered in real-world settings. Through experimental analysis, we demonstrate that DBTR successfully learns to quantify and estimate desired properties.

翻译：现实世界中的许多属性无法直接观测，导致其难以学习。为应对这一挑战，以往研究主要通过将分级人工评分作为训练目标标签来估计这些属性。与此同时，基于布拉德利-特里模型的评分算法已被广泛研究，用于根据比赛历史评估选手的竞争力。本文提出深度布拉德利-特里评分（DBTR），这是一种新颖的机器学习框架，旨在量化和评估未知物品的属性。我们的方法将布拉德利-特里模型无缝集成到神经网络结构中。此外，我们进一步将该架构推广到存在不公平性的非对称环境——这种条件在现实场景中更为常见。通过实验分析，我们证明DBTR能够成功学习量化和估计所需属性。