Enhancing user experience of task assignment in spatial crowdsourcing: A Self-Adaptive Batching Approach

Lai Qian, Guanfeng Liu, Fei Zhu, Zhixu Li*, Yu Wang, An Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
15 Downloads (Pure)


Faced with the explosive demand of real-world applications, spatial crowdsourcing has attracted much attention, in which task assignment algorithms take the dominant role in the past few years. On the one hand, most recent studies concentrate on maximizing the overall benefits of the platform, ignoring the fact that user experience also plays an essential role in task allocation. On the other hand, they focus on matching, that is, how to assign tasks, rather than batching, that is, when to make assignment. In fact, user experience also depends on batching, but this is largely overlooked by current studies. In this paper, we propose a self-adaptive batching mechanism to enhance user experience in spatial crowdsourcing. With appropriate start-up timestamps, previous matching methods can perform better. Multi-armed bandit algorithm in reinforcement learning is adopted to split the batch dynamically according to historical current states. Extensive experimental results on both real and synthetic datasets demonstrate the effectiveness and efficiency of the proposed approach.

Original languageEnglish
Pages (from-to)132324-132332
Number of pages9
JournalIEEE Access
Publication statusPublished - 2019

Bibliographical note

Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • Spatial crowdsourcing
  • online task assignment
  • user experience
  • self-adaptive batching
  • multi-armed bandit


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