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| Content Provider | ACM Digital Library |
|---|---|
| Author | Schneider, Jon Chen, Xi Mao, Jieming Gopi, Sivakanth |
| 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 [EQUATION]; i.e. to solve any instance of top-K, our algorithm needs at most [EQUATION] 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 O(n) or worse). We further show that this is tight: any algorithm for the top-K problem has competitive ratio at least [EQUATION]. |
| Starting Page | 1245 |
| Ending Page | 1264 |
| Page Count | 20 |
| File Format | |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-01-16 |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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