  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.
