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山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (2): 128-134.doi: 10.6040/j.issn.1672-3961.0.2021.548

• • 上一篇    

时空众包下复合选择的长期激励算法

张琦,王莹洁*   

  1. 烟台大学计算机与控制工程学院, 山东 烟台 264000
  • 发布日期:2022-04-20
  • 作者简介:张琦(1998— ),男,山东潍坊人,硕士研究生,主要研究方向为群智感知,任务分配. E-mail:zhangzy_123@126.com. *通信作者简介:王莹洁(1986— ),女,吉林德惠人,副教授,博士,主要研究方向为群智感知网络,人工智能. E-mail:towangyingjie@163.com.
  • 基金资助:
    国家自然科学基金资助项目(61822602,61772207,61802331,61702439,61773331);中国博士后基金资助项目(2019T120732,2017M622691)

Long-termincentive algorithm for compound selection under spatio-temporal crowdsourcing

ZHANG Qi, WANG Yingjie*   

  1. School of Computer and Control Engineering, Yantai University, Yantai 264005, Shandong, China
  • Published:2022-04-20

摘要: 采用长期激励模型应对时空众包下的工人激励问题,提出一种基于博弈论的长期激励算法(multi-stage compound selection, MSCS)。工人的激励模型考虑用户对任务的兴趣、任务奖励和对长期参与的参与度3部分内容,利用激励模型对工人进行长期激励。通过计算用户参与众包过程的最佳次数,制定对用户的个性化激励策略。采用用户的数量和最大平均参与度与其他长期激励算法以及基线算法进行对比分析。试验结果证明,MSCS算法在相同预算下能够激励更多的用户参与众包过程,在预算不足的情况下也能吸引用户更长时间的参与众包过程。MSCS算法具有更好的长期激励效果。

关键词: 时空众包, 激励机制, 长期激励, 用户兴趣, 最大平均参与度

中图分类号: 

  • TP39
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