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- W2906702016 abstract "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 $tilde{O}(sqrt{n})$; i.e. to solve any instance of top-$K$, our algorithm needs at most $tilde{O}(sqrt{n})$ 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 $tilde{Omega}(n)$ or worse). We further show that this is tight: any algorithm for the top-$K$ problem has competitive ratio at least $tilde{Omega}(sqrt{n})$." @default.
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- W2906702016 date "2016-05-12" @default.
- W2906702016 modified "2023-09-23" @default.
- W2906702016 title "Competitive analysis of the top-K ranking problem" @default.
- W2906702016 doi "https://doi.org/10.48550/arxiv.1605.03933" @default.
- W2906702016 hasPublicationYear "2016" @default.
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