This contribution introduces a novel statistical learning methodology based on the Bradley-Terry method for pairwise comparisons, where the novelty arises from the method's capacity to estimate the worth of objects for a primary attribute by incorporating data of secondary attributes. These attributes are properties on which objects are evaluated in a pairwise fashion by individuals. By assuming that the main interest of practitioners lies in the primary attribute, and the secondary attributes only serve to improve estimation of the parameters underlying the primary attribute, this paper utilises the well-known transfer learning framework. To wit, the proposed method first estimates a biased worth vector using data pertaining to both the primary attribute and the set of informative secondary attributes, which is followed by a debiasing step based on a penalised likelihood of the primary attribute. When the set of informative secondary attributes is unknown, we allow for their estimation by a data-driven algorithm. Theoretically, we show that, under mild conditions, the $\ell_\infty$ and $\ell_2$ rates are improved compared to fitting a Bradley-Terry model on just the data pertaining to the primary attribute. The favourable (comparative) performance under more general settings is shown by means of a simulation study. To illustrate the usage and interpretation of the method, an application of the proposed method is provided on consumer preference data pertaining to a cassava derived food product: eba. An R package containing the proposed methodology can be found on xhttps://CRAN.R-project.org/package=BTTL.

翻译：本研究基于Bradley-Terry成对比较方法，提出了一种新颖的统计学习方法。该方法的新颖性在于能够通过整合次要属性的数据来估计对象在主要属性上的价值。这些属性是评估者以成对方式评价对象时所依据的特性。本文假设实践者的主要兴趣在于主要属性，而次要属性仅用于改进主要属性底层参数的估计，因此采用了广为人知的迁移学习框架。具体而言，所提出的方法首先利用与主要属性和一组信息性次要属性相关的数据估计一个有偏的价值向量，随后基于主要属性的惩罚似然进行去偏步骤。当信息性次要属性集合未知时，我们允许通过数据驱动算法对其进行估计。理论上证明，在温和条件下，与仅使用主要属性数据拟合Bradley-Terry模型相比，该方法在$\ell_\infty$和$\ell_2$范数下的收敛速率均得到提升。通过模拟研究展示了该方法在更一般设置下的优越（比较）性能。为说明该方法的使用与解释，本文将其应用于关于木薯衍生食品——eba的消费者偏好数据。包含该方法的R软件包可在xhttps://CRAN.R-project.org/package=BTTL获取。