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

With the ubiquity of smart devices, Spatial Crowdsourcing (SC) has emerged as a new transformative platform engaging mobile users to perform tasks by physically traveling to specified locations. In this paper, we propose a novel preference-aware task assignment system based on workers' temporal preferences, which includes 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. Supplementing 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, where we give higher priorities to workers who are more interested in the tasks. 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, where workers' tolerable waiting time and consensus are considered. We conduct extensive experiments using a real dataset, verifying the practicability of the methods.