Efficient task assignment for spatial crowdsourcing: A combinatorial fractional optimization approach with semi-bandit learning
作者：
Highlights：
• We introduce min-cost max-reliability assignment problem in spatial crowdsourcing.
• We propose a novel distance-reliability ratio approach to address the problem.
• We extend the proposed approach for dynamic estimation of worker reliabilities.
• We present the performance of algorithms on synthetic and real-world datasets.
• The proposed approach achieves lower travel costs while maximizing the reliability.
摘要
•We introduce min-cost max-reliability assignment problem in spatial crowdsourcing.•We propose a novel distance-reliability ratio approach to address the problem.•We extend the proposed approach for dynamic estimation of worker reliabilities.•We present the performance of algorithms on synthetic and real-world datasets.•The proposed approach achieves lower travel costs while maximizing the reliability.
论文关键词：Spatial crowdsourcing,Task assignment,Combinatorial fractional programming,Multi-armed bandit
论文评审过程：Received 29 June 2015, Revised 10 March 2016, Accepted 10 March 2016, Available online 1 April 2016, Version of Record 13 April 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.03.022
