中文摘要：

因特网上的任务任务广泛地被用于许多区域，例如，联机劳动市场，联机纸评论和聚会活动组织。在这份报纸，我们涉及与联机劳动市场有关的任务任务问题，作为 ClusterHire 称为。我们改进 ClusterHire 问题的定义，并且建议一个有效、有效的算法，有权享受的影响。另外，我们把参予限制放在 ClusterHire 上。以便阻止所有成员过度工作抑制每位专家的负担。为抑制参予的 ClusterHire 问题，我们设计二个算法，命名 ProjectFirst 和时代。前者由把专家加到一个起始的队产生一个 participationconstrained 队，并且后者由与最小的影响把专家从专家的宇宙移开产生一个抑制参予的队。试验性的评估显示那 1 ) 影响以有效性和时间效率比最先进的算法更好表现；2 ) ProjectFirst 以时间效率比时代更好表现，然而，时代以有效性比 ProjectFirst 更好表现。

英文摘要：

The task assignment on the Internet has been widely applied to many areas, e.g., online labor market, online paper review and social activity organization. In this paper, we are concerned 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 efficient 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 efficiency; 2） PROJECTFIRST performs better than ERA in terms of time efficiency, yet ERA performs better than PROJECTFIRST in terms of effectiveness.