Group Task Assignment with Social Impact-Based Preference in Spatial Crowdsourcing

Abstract

With the pervasiveness of GPS-enabled smart devices and increased wireless communication technologies, Spatial Crowdsourcing (SC) has drawn increasing attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, for the complex tasks, SC is more likely to assign each task to more than one worker, called Group Task Assignment (GTA), for the reason that an individual worker cannot complete the task well by herself. It is a challenging issue to assign worker groups the tasks that they are interested in and willing to perform. In this paper, we propose a novel framework for group task assignment based on worker groups’ preferences, which includes two components: Social Impact-based Preference Modeling (SIPM) and Preference-aware Group Task Assignment (PGTA). SIPM employs a Bipartite Graph Embedding Model (BGEM) and the attention mechanism to learn the social impact-based preferences of different worker groups on different task categories. PGTA utilizes an optimal task assignment algorithm based on the tree-decomposition technology to maximize the overall task assignments, in which we give higher priorities to the worker groups showing more interests in the tasks. Our empirical studies based on a real-world dataset verify the practicability of our proposed framework.