Bilateral Preference-aware Task Assignment in Spatial Crowdsourcing
Xu Zhou, Shiting Liang, Kenli Li, Yunjun Gao, Keqin Li
Abstract
Task assignment is a crucial issue in spatial crowd-sourcing. In most existing studies, the results of the task assignment cannot satisfy the workers and tasks at the same time. This is because only one-sided preferences are taken into account. Moreover, tasks are always assigned based on the locations of workers instead of the trajectories. Accordingly, they are not appropriate to the specific applications, such as carpool. Inspired by this, we investigate an interesting problem of task assignment, namely bilateral preference-aware task assignment (BPTA), with the goal of maximizing the overall satisfaction of workers and tasks by assigning tasks to suitable workers based on their routine trajectories. To tackle this problem effectively, we first propose greedy algorithms, namely task preference priority greedy and worker preference priority greedy algorithms, which are task-driven and worker-driven, respectively. Although these algorithms can solve the BPTA problem effectively, they cannot ensure the stability of the task assignment results. In other words, there can be better choices for some workers and tasks. Accordingly, we further explore deferred acceptance algorithms to find a stable matching for workers and tasks by simultaneously considering the preferences of workers and tasks. Moreover, two optimizing strategies, including a parallel strategy and a top- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> strategy, are introduced to boost the performance in handling the BPTA problem. Extensive experiments on both real and synthetic datasets have validated the efficiency and effectiveness of our proposed algorithms.