Crowdsourced Top-k Queries by Confidence-Aware Pairwise Judgments
Summary: Crowdsourced top-k processing via confidence-aware pairwise judgments; uses t-distribution and Stein estimators to bound per-comparison confidence. Under Select-Partition-Rank, minimizes cost; four real datasets show superiority to prior top-k methods. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ngai Meng Kou (University of Macau)
- 2. Yan Li (University of Macau)
- 3. Hao Wang (Nanjing University)
- 4. Leong Hou U (University of Macau)
- 5. Zhiguo Gong (University of Macau)
BibTeX Citation
@inproceedings{kou_sigmod17,
title = {{Crowdsourced Top-k Queries by Confidence-Aware Pairwise Judgments}},
author = {Kou, Ngai Meng and Li, Yan and Wang, Hao and U, Leong Hou and Gong, Zhiguo},
series = {{SIGMOD} '17},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3035918.3035953},
url = {https://dl.acm.org/doi/10.1145/3035918.3035953},
year = {2017}
}Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,478 | Top-k Sorting Under Partial Order Information | 2018 | SIGMOD | 6.5814419e-05 |
| 5,804 | Top-K Deep Video Analytics: A Probabilistic Approach | 2021 | SIGMOD | 5.9888455e-05 |
| 10,006 | Hierarchical Entity Resolution using an Oracle | 2022 | SIGMOD | 5.0979044e-05 |
| 10,008 | How to Design Robust Algorithms using Noisy Comparison Oracle | 2021 | VLDB | 5.0979044e-05 |
| 11,491 | k-Clustering with Comparison and Distance Oracles | 2024 | PODS | 4.9793485e-05 |
| 12,212 | A Rating-Ranking Method for Crowdsourced Top-k Computation | 2018 | SIGMOD | 4.9793485e-05 |
| 12,308 | A Confidence-Aware Top-k Query Processing Toolkit on Crowdsourcing | 2017 | VLDB | 4.9793485e-05 |
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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 |
|---|---|---|---|---|
| 266 | Human-powered Sorts and Joins | 2012 | VLDB | 0.00022739124 |
| 749 | So Who Won? Dynamic Max Discovery with the Crowd | 2012 | SIGMOD | 0.00014265279 |
| 871 | Leveraging Transitive Relations for Crowdsourced Joins | 2013 | SIGMOD | 0.00013343705 |
| 2,832 | Crowd Mining | 2013 | SIGMOD | 7.9590386e-05 |
| 3,197 | iCrowd: An Adaptive Crowdsourcing Framework | 2015 | SIGMOD | 7.5455503e-05 |
| 5,024 | Crowdsourced Top-k Algorithms: An Experimental Evaluation | 2016 | VLDB | 6.3096186e-05 |
| 5,466 | Crowdsourcing Applications and Platforms: A Data Management Perspective | 2011 | VLDB | 6.1202777e-05 |
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| 6 | 3,105 | Top-k Queries on Uncertain Data: On Score Distribution and Typical Answers | 2009 | SIGMOD |
| 7 | 1,535 | Top-k Query Evaluation with Probabilistic Guarantees | 2004 | VLDB |
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| 10 | 12,308 | A Confidence-Aware Top-k Query Processing Toolkit on Crowdsourcing | 2017 | VLDB |