Recruitment From Social Networks for the Cold Start Problem in Mobile Crowdsourcing

Mobile crowdsourcing (MCS) endeavors to attain reliable truth by recruiting large numbers of users with handheld mobile devices to collect data. However, during the early stages of platform development, MCS encounters the cold start problem, failing to complete the task. Existing research addresses this issue by leveraging social networks for user recruitment; nevertheless, there is a predominant focus on user quantity, and the quality of task completion is ignored. Additionally, fairness considerations among users are lacking. Therefore, this paper proposes Recruitment based on Social Users’ Trust (RSUT) to solve the cold start problem while maintaining high task completion quality. Specifically, we propose the activation model based on user awareness to simulate the influence of social users and task attributes on activation from the perspective of unregistered users, which is more realistic. Additionally, we measure the user’s contribution and then design a reward system based on the user’s contribution to ensure fairness. Finally, social network-based trust evaluation is proposed to identify malicious users and update rewards in real time according to task requirements to ensure high-quality completion of tasks within budget constraints. Extensive experimental results demonstrate the superior performance of RSUT compared to state-of-the-art methods in task completion quality, user recruitment, and task completion rate.