Name
Abstract | ||
|---|---|---|
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 to dynamically sample subsets of labelers based on an exploration/exploitation criterion. The algorithm produces a weighted combination of a subset of the labelers' votes that approximates the crowd's opinion. |
Year | Venue | Field |
|---|---|---|
2012 | CoRR | Data mining,Crowdsourcing,Computer science,Wisdom of crowds,Artificial intelligence,Majority opinion,Machine learning |
DocType | Volume | Citations |
Journal | abs/1204.3611 | 9 |
PageRank | References | Authors |
0.92 | 21 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seyda Ertekin | 1 | 623 | 36.13 |
| Haym Hirsh | 2 | 1839 | 277.74 |
| Cynthia Rudin | 3 | 720 | 61.51 |