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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Aris Anagnostopoulos (Sapienza University)
- 2. Luca Becchetti (Sapienza University)
- 3. Adriano Fazzone (Sapienza University)
- 4. Ida Mele (Max Planck Institute)
- 5. Matteo Riondato (Stanford University)
BibTeX Citation
@inproceedings{anagnostopoulos_sigmod15,
title = {{The Importance of Being Expert: Efficient Max-Finding in Crowdsourcing}},
author = {Anagnostopoulos, Aris and Becchetti, Luca and Fazzone, Adriano and Mele, Ida and Riondato, Matteo},
series = {{SIGMOD} '15},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2723372.2723722},
url = {https://dl.acm.org/doi/10.1145/2723372.2723722},
year = {2015}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
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 |
|---|---|---|---|---|
| 90 | CrowdDB: Answering Queries with Crowdsourcing | 2011 | SIGMOD | 0.00034951786 |
| 251 | Crowdsourced Databases: Query Processing with People | 2011 | CIDR | 0.00023261113 |
| 265 | Human-powered Sorts and Joins | 2012 | VLDB | 0.00022935368 |
| 743 | So Who Won? Dynamic Max Discovery with the Crowd | 2012 | SIGMOD | 0.00014421358 |
| 997 | CrowdScreen: Algorithms for Filtering Data with Humans | 2012 | SIGMOD | 0.00012755983 |
| 3,914 | Optimal Crowd-Powered Rating and Filtering Algorithms | 2014 | VLDB | 7.0204052e-05 |
| 4,153 | Whom to Ask? Jury Selection for Decision Making Tasks on Micro-blog Services | 2012 | VLDB | 6.8676997e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 11,847 | Recommending Deployment Strategies in Crowdsourcing Platforms | 2019 | SIGMOD |
| 2 | 3,262 | iCrowd: An Adaptive Crowdsourcing Framework | 2015 | SIGMOD |
| 3 | 7,222 | Hear the Whole Story: Towards the Diversity of Opinion in Crowdsourcing Markets | 2015 | VLDB |
| 4 | 743 | So Who Won? Dynamic Max Discovery with the Crowd | 2012 | SIGMOD |
| 5 | 997 | CrowdScreen: Algorithms for Filtering Data with Humans | 2012 | SIGMOD |
| 6 | 4,915 | Crowdsourced Top-k Algorithms: An Experimental Evaluation | 2016 | VLDB |
| 7 | 3,914 | Optimal Crowd-Powered Rating and Filtering Algorithms | 2014 | VLDB |
| 8 | 5,143 | An Online Cost Sensitive Decision-Making Method in Crowdsourcing Systems | 2013 | SIGMOD |
| 9 | 5,813 | Efficient Algorithms for Crowd-Aided Categorization | 2020 | VLDB |
| 10 | 8,351 | Minimizing Efforts in Validating Crowd Answers | 2015 | SIGMOD |