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Crowdsourced Top-k Algorithms: An Experimental Evaluation

Summary: Compares crowdsourced top-k algorithms under a unified experimental framework across synthetic and real data. Focus on result quality and efficiency on real crowdsourcing platforms; reveals when DB heuristics vs ML methods excel and provides algorithm selection guidelines. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11351
Venue
VLDB
Year
2016
Pagerank
6.070469e-05
Overall Rank
4,579 | 68.15%
DOI
-

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
267 Human-powered Sorts and Joins 2012 VLDB 0.00029690405
859 So Who Won? Dynamic Max Discovery with the Crowd 2012 SIGMOD 0.00015870894
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