Motivated by applications in recommender systems, web search, social choice and crowdsourcing, we consider the problem of identifying the set of top K items from noisy pairwise comparisons. In our setting, we are non-actively given r pairwise comparisons between each pair of n items, where each comparison has noise constrained by a very general noise model called the strong stochastic transitivity (SST) model. We analyze the competitive ratio of algorithms for the top-K problem. In particular, we present a linear time algorithm for the top-K problem which has a competitive ratio of O( \u221a n); i.e. to solve any instance of top-K, our algorithm needs at most O( \u221a n) times as many samples needed as the best possible algorithm for that instance (in contrast, all previous known algorithms for the top-K problem have competitive ratios of \u03a9(n) or worse). We further show that this is tight: any algorithm for the top-K problem has competitive ratio at least \u03a9( \u221a n). Stern School of Business, New York University, email: xchen3@stern.nyu.edu Department of Computer Science, Princeton University, email: sgopi@cs.princeton.edu Department of Computer Science, Princeton University, email: jiemingm@cs.princeton.edu Department of Computer Science, Princeton University, email: js44@cs.princeton.edu 1<\/p>\n","protected":false},"excerpt":{"rendered":"
Motivated by applications in recommender systems, web search, social choice and crowdsourcing, we consider the problem of identifying the set of top K items from noisy pairwise comparisons. 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