Many properties in the real world, such as desirability or strength in competitive environment, can't be directly observed, which makes them difficult to evaluate. To deal with this challenging problem, prior works have primarily focused on estimating those properties of known items, especially the strength of sports players, only of those who appears in paired comparison dataset. In this paper, we introduce Deep Bradley-Terry Rating (DBTR), a novel ML framework to evaluate any properties of unknown items, not necessarily present in the training data. Our method seamlessly integrates traditional Bradley-Terry model with a neural network structure. We also generalizes this architecture further for asymmetric environment with unfairness, which is much more common in real world settings. In our experimental analysis, DBTR successfully learned desired quantification of those properties.

翻译：现实世界中的许多属性，例如竞争环境中的可取性或强度，无法被直接观测，这使得它们难以评估。为应对这一挑战性问题，先前的工作主要集中于估计已知物品的属性，尤其是体育运动员的强度——仅限于出现在配对比较数据集中的个体。本文提出深度布雷德利-特里评分（DBTR），一种新颖的机器学习框架，用于评估未知物品（未必存在于训练数据中）的任意属性。我们的方法将传统布雷德利-特里模型与神经网络结构无缝融合，并进一步将该架构推广至具有不公平性的非对称环境——这在现实世界场景中更为常见。实验分析表明，DBTR成功学会了这些属性的期望量化。