The Importance of Being Expert: Efficient Max-Finding in Crowdsourcing
Summary: Two-class crowdsourcing model (experts vs. naive) with a threshold error framework for evaluating accuracy-cost tradeoffs. Proposes a max-finding algorithm achieving a constant-factor approximation with expert and naive workers, and tight upper/lower bounds, validated on CrowdFlower data. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Aris Anagnostopoulos
- 2. Luca Becchetti
- 3. Adriano Fazzone
- 4. Ida Mele
- 5. Matteo Riondato
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Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 94 | CrowdDB: Answering Queries with Crowdsourcing | 2011 | SIGMOD | 0.00051013264 |
| 249 | Crowdsourced Databases: Query Processing with People | 2011 | CIDR | 0.00030740523 |
| 267 | Human-powered Sorts and Joins | 2012 | VLDB | 0.00029690405 |
| 859 | So Who Won? Dynamic Max Discovery with the Crowd | 2012 | SIGMOD | 0.00015870894 |
| 1,164 | CrowdScreen: Algorithms for Filtering Data with Humans | 2012 | SIGMOD | 0.00013564823 |
| 4,479 | Optimal Crowd-Powered Rating and Filtering Algorithms | 2014 | VLDB | 6.149053e-05 |
| 4,651 | Whom to Ask? Jury Selection for Decision Making Tasks on Micro-blog Services | 2012 | VLDB | 6.022931e-05 |
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