Preference Modeling with Context-Dependent Salient Features
Preference Modeling with Context-Dependent Salient Features
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发表时间：
2020-02
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通讯作者：
Amanda Bower;L. Balzano
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作者：
Amanda Bower;L. Balzano
我们考虑在给定物品特征的情况下，从有噪声的成对比较中估计一组物品的排名问题。我们解决了成对比较数据往往反映出非理性选择（例如非传递性）这一事实。我们的关键观察结果是，在与其他物品隔离的情况下进行比较的两个物品可能仅基于特征的一个显著子集进行比较。将此框架形式化，我们提出了显著特征偏好模型，并证明了一个有限样本复杂度结果，用于通过最大似然估计学习我们模型的参数以及潜在排名。我们还提供了支持我们理论界限的实证结果，并说明了我们的模型如何解释系统性的非传递性。最后，我们展示了我们的模型在合成数据以及两个真实数据集（UT Zappos50K数据集和关于美国立法选区紧凑性的比较数据）上进行最大似然估计的强大性能。
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.