LAUSR

Multiple cooperative task assignment is a crucial problem in spatial crowdsourcing, in which each task requires more than one appropriate user to complete. Existing studies generally assume that all users are trustworthy and can reliably perform assigned tasks. However, such assumptions do not hold in the real world. In this paper, we consider an essential crowdsourcing problem, namely Reliability-oriented Socially-Aware Crowdsourcing (R-SAC), which aims to recruit reliable users for multiple cooperative tasks so that the overall reliability of task assignment is maximized. We prove that the R-SAC problem is NP-hard. Then, we propose an approximation algorithm and provide a theoretical guarantee. Specifically, user reliability refers to the probability that a user can reliably perform assigned tasks. To achieve reliable user recruitment during task assignment, we formulate the reliability of a user by combining the matching between the user and tasks, and the reliability feedback from neighbors who share similar behaviors with the user in a social network. Besides, the distributed collaborative filtering technique is utilized to select the reliability feedback from the neighbors. Experiments on two real-world datasets validate that our proposed approach significantly outperforms the representative approaches.