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Approximating the crowd
Date
2014-09-01
Author
Ertekin Bolelli, Şeyda
Hirsh, Haym
Metadata
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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The problem of "approximating the crowd" is that of estimating the crowd's majority opinion by querying only a subset of it. Algorithms that approximate the crowd can intelligently stretch a limited budget for a crowdsourcing task. We present an algorithm, "CrowdSense," that works in an online fashion where items come one at a time. CrowdSense dynamically samples subsets of the crowd based on an exploration/exploitation criterion. The algorithm produces a weighted combination of the subset's votes that approximates the crowd's opinion. We then introduce two variations of CrowdSense that make various distributional approximations to handle distinct crowd characteristics. In particular, the first algorithm makes a statistical independence approximation of the labelers for large crowds, whereas the second algorithm finds a lower bound on how often the current subcrowd agrees with the crowd's majority vote. Our experiments on CrowdSense and several baselines demonstrate that we can reliably approximate the entire crowd's vote by collecting opinions from a representative subset of the crowd.
Subject Keywords
Computer Networks and Communications
,
Information Systems
,
Computer Science Applications
URI
https://hdl.handle.net/11511/43802
Journal
DATA MINING AND KNOWLEDGE DISCOVERY
DOI
https://doi.org/10.1007/s10618-014-0354-1
Collections
Department of Computer Engineering, Article
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Ş. Ertekin Bolelli and H. Hirsh, “Approximating the crowd,”
DATA MINING AND KNOWLEDGE DISCOVERY
, pp. 1189–1221, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43802.