Reconstructing Hidden Permutations Using the Average-Precision (AP) Correlation Statistic

We study the problem of learning probabilistic models for permutations, where the order between highly ranked items in the observed permutations is more reliable (i.e., consistent in different rankings) than the order between lower ranked items, a typical phenomena observed in many applications such as web search results and product ranking. We introduce and study a variant of the Mallows model where the distribution is a function of the widely used Average-Precision (AP) Correlation statistic, instead of the standard Kendall’s tau distance. We present a generative model for constructing samples from this distribution and prove useful properties of that distribution. Using these properties we develop an efficient algorithm that provably computes an asymptotically unbiased estimate of the center permutation, and a faster algorithm that learns with high probability the hidden central permutation for a wide range of the parameters of the model. We complement our theoretical analysis with extensive experiments showing that unsupervised methods based on our model can precisely identify ground-truth clusters of rankings in real-world data. In particular, when compared to the Kendall’s tau based methods, our methods are less affected by noise in low-rank items.