We consider a pairwise comparisons model with n users and m items. Each user is shown a few pairs of items, and when a pair of items is shown to a user, he or she expresses a preference for one of the items based on a probabilistic model. The goal is to group users into clusters so that users within each cluster have similar preferences. We will present an algorithm which clusters all users correctly with high probability using a number of pairwise comparisons which is within a polylog factor of an information-theoretic lower bound. Joint work with Barbara Dembin and Siddhartha Satpathi.

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