Preference-aware task assignment in spatial crowdsourcing: from individuals to groups

Yan Zhao, Kai Zheng*, Hongzhi Yin, Guanfeng Liu, Junhua Fang, Xiaofang Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

With the ubiquity of smart devices, Spatial Crowdsourcing (SC) has emerged as a new transformative platform that engages mobile users to perform spatio-temporal tasks by physically traveling to specified locations. Thus, various SC techniques have been studied for performance optimization, among which one of the major challenges is how to assign workers the tasks that they are really interested in and willing to perform. In this paper, we propose a novel preference-aware spatial task assignment system based on workers’ temporal preferences, which consists of two components: History-based Context-aware Tensor Decomposition (HCTD) for workers’ temporal preferences modeling and preference-aware task assignment. We model workers’ preferences with a three-dimension tensor (worker-task-time). Supplementing the missing entries of the tensor through HCTD with the assistant of historical data and other two context matrices, we recover workers’ preferences for different categories of tasks in different time slots. Several preference-aware individual task assignment algorithms are then devised, aiming to maximize the total number of task assignments at every time instance, in which we give higher priorities to the workers who are more interested in the tasks. In order to make our proposed framework applicable to more scenarios, we further optimize the original framework by proposing strategies to allow each task to be assigned to a group of workers such that the task can be completed by these workers simultaneously, wherein workers’ tolerable waiting time, consensus, and tasks’ rewards are taken into consideration. We conduct extensive experiments using a real dataset, verifying the practicability of our proposed methods.

Original languageEnglish
Pages (from-to)3461-3477
Number of pages17
JournalIEEE Transactions on Knowledge and Data Engineering
Volume34
Issue number7
Early online date1 Sep 2020
DOIs
Publication statusPublished - Jul 2022

Keywords

  • Computer science
  • Crowdsourcing
  • Electronic mail
  • History
  • Performance evaluation
  • Spatial Crowdsourcing
  • Task analysis
  • Task Assignment
  • Tensile stress
  • Worker Preference

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