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Dynamic Pricing in Spatial Crowdsourcing: A Matching-Based Approach

Summary: Defines Global Dynamic Pricing (GDP) for spatial crowdsourcing across interdependent local markets. Introduces MAPS, a matching-based pricing algorithm with a provable bound that approximates revenue under limited supply and market coupling. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5637
Venue
SIGMOD
Year
2018
Pagerank
5.2494989e-05
Overall Rank
9,597 | 34.16%
DOI
10.1145/3183713.3196929

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Authors

BibTeX Citation

@inproceedings{tong_sigmod18,
        title = {{Dynamic Pricing in Spatial Crowdsourcing: A Matching-Based Approach}},
        author = {Tong, Yongxin and Wang, Libin and Zhou, Zimu and Chen, Lei and Du, Bowen and Ye, Jieping},
        series = {{SIGMOD} '18},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3183713.3196929},
        url = {https://dl.acm.org/doi/10.1145/3183713.3196929},
        year = {2018}
}

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