While preference modelling is becoming one of the pillars of machine learning, the problem of preference explanation remains challenging and underexplored. In this paper, we propose \textsc{Pref-SHAP}, a Shapley value-based model explanation framework for pairwise comparison data. We derive the appropriate value functions for preference models and further extend the framework to model and explain \emph{context specific} information, such as the surface type in a tennis game. To demonstrate the utility of \textsc{Pref-SHAP}, we apply our method to a variety of synthetic and real-world datasets and show that richer and more insightful explanations can be obtained over the baseline.

翻译：虽然偏好建模正在成为机器学习的支柱之一,但偏好解释的问题仍然具有挑战性和探索不足。 在本文中,我们提议了\ textsc{Pref-SHAP},这是一个基于价值的模型解释框架,用于配对比较数据。我们为偏好模型得出适当的价值功能,并将框架进一步扩展至模型并解释\emph{context 特定}信息,例如网球游戏中的表层类型。为了证明\ textsc{Pref-SHAP}的效用,我们将我们的方法应用于各种合成和现实世界数据集,并表明可以在基线上获得更丰富和更有洞察力的解释。