Top-k Sorting Under Partial Order Information
Summary: Top-k sorting under partial orders with crowdsourced comparisons; minimizes expert workload. A dedicated top-k algorithm for PO, valid under two notions of the comparator, improving learning-to-rank with synthetic and real data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Eyal Dushkin (Tel Aviv University)
- 2. Tova Milo (Tel Aviv University)
BibTeX Citation
@inproceedings{dushkin_sigmod18,
title = {{Top-k Sorting Under Partial Order Information}},
author = {Dushkin, Eyal and Milo, Tova},
series = {{SIGMOD} '18},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3183713.3199672},
url = {https://dl.acm.org/doi/10.1145/3183713.3199672},
year = {2018}
}Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,313 | Interactive Graph Search | 2019 | SIGMOD | 5.6480084e-05 |
| 9,812 | Interactive Graph Search for Multiple Targets on DAGs | 2025 | VLDB | 5.214913e-05 |
| 9,818 | Hierarchical Entity Resolution using an Oracle | 2022 | SIGMOD | 5.214913e-05 |
| 9,820 | How to Design Robust Algorithms using Noisy Comparison Oracle | 2021 | VLDB | 5.214913e-05 |
| 11,143 | k-Clustering with Comparison and Distance Oracles | 2024 | PODS | 5.093636e-05 |
| 11,250 | Robust Best Point Selection under Unreliable User Feedback | 2024 | VLDB | 5.093636e-05 |
| 11,705 | TQEL: Framework for Query-Driven Linking of Top-K Entities in Social Media Blogs | 2021 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 635 | Evaluating Top-k Selection Queries | 1999 | VLDB | 0.00015527042 |
| 743 | So Who Won? Dynamic Max Discovery with the Crowd | 2012 | SIGMOD | 0.00014421358 |
| 1,327 | A Unified Approach to Ranking in Probabilistic Databases | 2009 | VLDB | 0.00011141552 |
| 1,844 | Probabilistic Ranking of Database Query Results | 2004 | VLDB | 9.6314187e-05 |
| 2,219 | Chimera: Large-Scale Classification using Machine Learning, Rules, and Crowdsourcing | 2014 | VLDB | 8.9303727e-05 |
| 3,971 | CLAMShell: Speeding up Crowds for Low-latency Data Labeling | 2016 | VLDB | 6.9835263e-05 |
| 4,821 | Crowdsourced Top-k Queries by Confidence-Aware Pairwise Judgments | 2017 | SIGMOD | 6.4935866e-05 |
| 4,915 | Crowdsourced Top-k Algorithms: An Experimental Evaluation | 2016 | VLDB | 6.4477856e-05 |
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| 2 | 2,827 | Efficient Processing of Top-k Dominating Queries on Multi-Dimensional Data | 2007 | VLDB |
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| 5 | 12,013 | A Confidence-Aware Top-k Query Processing Toolkit on Crowdsourcing | 2017 | VLDB |
| 6 | 12,308 | Optimal Top-k Generation of Attribute Combinations based on Ranked Lists | 2012 | SIGMOD |
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| 10 | 4,915 | Crowdsourced Top-k Algorithms: An Experimental Evaluation | 2016 | VLDB |