Recommending Deployment Strategies in Crowdsourcing Platforms
Summary: Optimization-based formalism for recommending deployment strategies in crowdsourcing. A geometric solution returns k strategies matching a deployment-parameter query; if none exist, it proposes the closest query yielding k strategies, with empirical evaluation. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Dong Wei (New Jersey Institute of Technology)
BibTeX Citation
@inproceedings{wei_sigmod19,
title = {{Recommending Deployment Strategies in Crowdsourcing Platforms}},
author = {Wei, Dong},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3300106},
url = {https://dl.acm.org/doi/10.1145/3299869.3300106},
year = {2019}
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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 |
|---|---|---|---|---|
| 18 | How Good Are Query Optimizers, Really? | 2016 | VLDB | 0.00059284255 |
| 4,076 | A Probabilistic Optimization Framework for the Empty-Answer Problem | 2013 | VLDB | 6.9221203e-05 |
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