In mobile crowdsensing, numerous smartphone users fulfill a complex environmental or social task in a cooperative way, in which participant recruitment or task allocation is a fundamental issue. Various mechanisms have been proposed to motivate normal users to participate in sensing tasks or provide high-quality sensing data. However, there exist some conflicts among tasks and participants, which make participant recruitment a challenging issue. For this issue, a conflict-aware participant recruitment (CAPR) mechanism is proposed for mobile crowdsensing, where there may exist task correlations and conflicts. First, two definitions of conflicts are introduced, and then, the participant recruitment problem is formalized. Next, an efficient heuristic algorithm is proposed followed by the payment determination and reputation update of participants. Simulation results indicate that the proposed mechanism can effectively improve the platform utility and the average task quality while guaranteeing no conflicts in fulfilling sensing tasks.