A Rating-Ranking Method for Crowdsourced Top-k Computation
Summary: Introduces rating-ranking crowdsourcing for top-k, avoiding quadratic pairwise comparisons. A unified model blends coarse rating and fine ranking questions with query selection/assignment, yielding cost-efficient, accurate top-k results. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Kaiyu Li (Tsinghua University)
- 2. Xiaohang Zhang (Tsinghua University)
- 3. Guoliang Li (Tsinghua University)
BibTeX Citation
@inproceedings{li_sigmod18,
title = {{A Rating-Ranking Method for Crowdsourced Top-k Computation}},
author = {Li, Kaiyu and Zhang, Xiaohang and Li, Guoliang},
series = {{SIGMOD} '18},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3183713.3183762},
url = {https://dl.acm.org/doi/10.1145/3183713.3183762},
year = {2018}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,899 | Human-in-the-loop Outlier Detection | 2020 | SIGMOD | 5.7431609e-05 |
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