The task assignment on the Intet has been widely applied to many areas, e.g., online labor market, online paper review and social activity organization. In this paper, we are conced with the task assignment problem related to the online labor market, termed as ClusterHire. We improve the definition of the ClusterHire problem, and propose an e?cient and effective algorithm, entitled Influence. In addition, we place a participation constraint on ClusterHire. It constrains the load of each expert in order to keep all members from overworking. For the participation-constrained ClusterHire problem, we devise two algorithms, named ProjectFirst and Era. The former generates a participation-constrained team by adding experts to an initial team, and the latter generates a participation-constrained team by removing the experts with the minimum influence from the universe of experts. The experimental evaluations indicate that 1) Influ-ence performs better than the state-of-the-art algorithms in terms of effectiveness and time e?ciency; 2) ProjectFirst performs better than Era in terms of time e?ciency, yet Era performs better than ProjectFirst in terms of effectiveness.