Fine-Grained Recommendation Mechanism to Curb Astroturfing in Crowdsourcing Systems

Crowdsourcing activities, carrying out large-scale tasks via wisdoms of crowds, are widely used in practice. However, it is hard for users to find tasks that are suitable for them. Thus, many users participate in tasks, and they are not good at or not interested in, and give answers carelessly or randomly. This phenomenon causes heavy astroturfing problem in crowdsourcing systems, which not only hurts the quality of completing tasks, but also influences user experience. Therefore, recommendation mechanisms that can optimize the match between users and tasks are in demand. However, existing studies simply adopt users' expertise level or interest degree as the key rule for recommendation. They neglect the fact that interest and expertise function jointly, and that interest can sometimes exert reaction force on expertise. Besides, previous studies assume that users' interest degree is steady, yet ignoring that it is time-varying rather than static in real world. In this paper, we propose IntexCrowd, fine-grained recommendation mechanism through interest-expertise collaborative awareness for crowdsourcing systems, to curb astroturfing problem. First, the IntexCrowd assigns a topic to each task. Then, topic-specific expertise level as well as interest degree of users are estimated according to historical records of tasks. At last, suitable user lists for topic-specific tasks are suggested as recommendation results. And we present a case study and a set of experiments to confirm the validity of IntexCrowd.