Label Partitioning for Sublinear Ranking

Abstract

We consider the case of ranking a very large set of labels, items, or documents, which is common to information retrieval, recommendation, and large-scale annotation tasks. We present a general approach for converting an algorithm which has linear time in the size of the set to a sublinear one via label partitioning. Our method consists of learning an input partition and a label assignment to each partition of the space such that precision at k is optimized, which is the loss function of interest in this setting. Experiments on large-scale ranking and recommendation tasks show that our method not only makes the original linear time algorithm computationally tractable, but can also improve its performance.