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
- 2. Xiaohang Zhang
- 3. Guoliang Li
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,575 | Human-in-the-loop Outlier Detection | 2020 | SIGMOD | 4.7068909e-05 |
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