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
- 2. Luca Becchetti
- 3. Adriano Fazzone
- 4. Ida Mele
- 5. Matteo Riondato
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 |
|---|---|---|---|---|
| 94 | CrowdDB: Answering Queries with Crowdsourcing | 2011 | SIGMOD | 0.00051273089 |
| 246 | Crowdsourced Databases: Query Processing with People | 2011 | CIDR | 0.00030952631 |
| 266 | Human-powered Sorts and Joins | 2012 | VLDB | 0.00029884758 |
| 854 | So Who Won? Dynamic Max Discovery with the Crowd | 2012 | SIGMOD | 0.00015879917 |
| 1,154 | CrowdScreen: Algorithms for Filtering Data with Humans | 2012 | SIGMOD | 0.00013616867 |
| 4,483 | Optimal Crowd-Powered Rating and Filtering Algorithms | 2014 | VLDB | 6.1431599e-05 |
| 4,648 | Whom to Ask? Jury Selection for Decision Making Tasks on Micro-blog Services | 2012 | VLDB | 6.01726e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,657 | Recommending Deployment Strategies in Crowdsourcing Platforms | 2019 | SIGMOD | 4.1905499e-05 |
| 3,324 | iCrowd: An Adaptive Crowdsourcing Framework | 2015 | SIGMOD | 7.2163115e-05 |
| 7,025 | Hear the Whole Story: Towards the Diversity of Opinion in Crowdsourcing Markets | 2015 | VLDB | 4.8530001e-05 |
| 854 | So Who Won? Dynamic Max Discovery with the Crowd | 2012 | SIGMOD | 0.00015879917 |
| 1,154 | CrowdScreen: Algorithms for Filtering Data with Humans | 2012 | SIGMOD | 0.00013616867 |
| 4,580 | Crowdsourced Top-k Algorithms: An Experimental Evaluation | 2016 | VLDB | 6.0646299e-05 |
| 4,483 | Optimal Crowd-Powered Rating and Filtering Algorithms | 2014 | VLDB | 6.1431599e-05 |
| 4,832 | An Online Cost Sensitive Decision-Making Method in Crowdsourcing Systems | 2013 | SIGMOD | 5.8883457e-05 |
| 5,744 | Efficient Algorithms for Crowd-Aided Categorization | 2020 | VLDB | 5.343155e-05 |
| 8,358 | Minimizing Efforts in Validating Crowd Answers | 2015 | SIGMOD | 4.5324924e-05 |