Profit Optimization in Spatial Crowdsourcing: Effectiveness and Efficiency-Reference-Cited by-同舟云学术

Profit Optimization in Spatial Crowdsourcing: Effectiveness and Efficiency

Published:2022 Issue: Volume: Page:1-16
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Zhao Yan¹^ORCID,Zheng Kai²^ORCID,Li Yunchuan²,Xia Jinfu³,Yang Bin¹^ORCID,Pedersen Torben Bach¹^ORCID,Mao Rui⁴^ORCID,Jensen Christian S.¹^ORCID,Zhou Xiaofang⁵^ORCID

Affiliation:

1. Department of Computer Science, Aalborg University, Aalborg, Denmark

2. School of Computer Science and Engineering & Shenzhen Institute for Advanced Study, University of Electronic Science and Technology China, China

3. Soochow University, Suzhou, China

4. Shenzhen University, Shenzhen, China

5. Hong Kong University of Science and Technology, Hong Kong, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/4358933/09941474.pdf?arnumber=9941474

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1. Task Recommendation in Spatial Crowdsourcing: A Trade-Off Between Diversity and Coverage;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

2. AdaTaskRec: An Adaptive Task Recommendation Framework in Spatial Crowdsourcing;ACM Transactions on Information Systems;2023-07-28

3. Batch-Based Cooperative Task Assignment in Spatial Crowdsourcing;2023 IEEE 39th International Conference on Data Engineering (ICDE);2023-04