Abstract
Proper incentive mechanism design for stimulating workers is a fundamental challenge in nowadays spatial crowdsourcing (SC) powered applications like Didi and Uber. Usually, extra monetary rewards are paid to workers as incentive to enhance their participation in the SC platform. However, deciding incentives in real-time is non-trivial as the spatial crowdsourcing market changes fast over time. Existing studies mostly assume an offline scenario where the incentives are computed considering a static market condition with the global knowledge of tasks and workers. Unfortunately, this setting does not fit the reality where the market itself would evolve gradually. In this paper, to enable online incentive determination, we formulate the problem of Real-time Monetary Incentive for Tasks in Spatial Crowdsourcing (MIT), which computes proper reward for each task to maximize the task completion rate at real time. We propose a unified and efficient approach to the MIT problem with a theoretical effectiveness guarantee. The experimental results on real ride-sharing data show that, compared with the state-of-the-art offline algorithms, our approach decreases the total worker response time by two orders of magnitude with insignificant utility loss.
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Notes
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If the problem is a minimization problem, change “\(\sup \)” to “\(\inf \)”.
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The data is available at https://outreach.didichuxing.com/research/opendata/.
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Acknowledgments
The work is partially supported by the Hong Kong RGC GRF Project 16207617, CRF project C6030-18G, AOE project AoE/E-603/18, the National Science Foundation of China (NSFC) under Grant No. 61729201, Science and Technology Planning Project of Guangdong Province, China, No. 2015B010110006, Hong Kong ITC Grants ITS/044/18FX and ITS/470/18FX, Didi-HKUST joint research lab Grant, Microsoft Research Asia Collaborative Research Grant, Wechat Research Grant and Webank Research Grant.
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Liu, Q., Zheng, L., Shen, Y., Chen, L. (2020). Finish Them on the Fly: An Incentive Mechanism for Real-Time Spatial Crowdsourcing. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12113. Springer, Cham. https://doi.org/10.1007/978-3-030-59416-9_45
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