Preference-Aware Task Assignment in Spatial Crowdsourcing

  • Yan Zhao Soochow University
  • Jinfu Xia Soochow University
  • Guanfeng Liu Macquarie University
  • Han Su University of Electronic Science and Technology of China
  • Defu Lian University of Electronic Science and Technology of China
  • Shuo Shang King Abdullah University of Science and Technology
  • Kai Zheng University of Electronic Science and Technology of China


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 worker 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 worker preferences for different categories of tasks in different time slots. Several preference-aware 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. We conduct extensive experiments using a real dataset, verifying the practicability of our proposed methods.

How to Cite
Zhao, Y., Xia, J., Liu, G., Su, H., Lian, D., Shang, S., & Zheng, K. (2019). Preference-Aware Task Assignment in Spatial Crowdsourcing. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 2629-2636.
AAAI Technical Track: Human-Computation and Crowd Sourcing