论文标题
具有与上下文相关的显着特征的偏好建模
Preference Modeling with Context-Dependent Salient Features
论文作者
论文摘要
我们考虑从给定的项目功能中估算一组项目的一组项目的排名的问题。我们解决了成对比较数据通常反映非理性选择的事实，例如不变性。我们的主要观察结果是，仅根据特征的显着子集比较与其他项目进行比较的两个项目。正式化此框架时，我们提出了显着特征偏好模型，并证明了学习模型参数以及具有最大似然估计的基础排名的有限样本复杂性结果。我们还提供了支持我们的理论界限的经验结果，并说明了我们的模型如何解释系统的不强调。最后，我们证明了我们在合成数据和两个实际数据集上对模型的最大似然估计的强劲性能：UT ZAPPOS50K数据集以及有关美国立法区紧凑性的比较数据。
We consider the problem of estimating a ranking on a set of items from noisy pairwise comparisons given item features. We address the fact that pairwise comparison data often reflects irrational choice, e.g. intransitivity. Our key observation is that two items compared in isolation from other items may be compared based on only a salient subset of features. Formalizing this framework, we propose the salient feature preference model and prove a finite sample complexity result for learning the parameters of our model and the underlying ranking with maximum likelihood estimation. We also provide empirical results that support our theoretical bounds and illustrate how our model explains systematic intransitivity. Finally we demonstrate strong performance of maximum likelihood estimation of our model on both synthetic data and two real data sets: the UT Zappos50K data set and comparison data about the compactness of legislative districts in the US.
