Crowdsourcing employs numerous users to perform certain tasks, in which task assignment is a challenging issue. Existing researches on task assignment mainly consider spatial–temporal diversity and capacity diversity, but not focus on the type diversity of users, which may lead to low quality of tasks. This paper formalizes a novel task assignment problem in crowdsourcing, where a task needs the cooperation of various types of users, and the quality of a task is highly related to the various types of the recruited users. Therefore, the goal of the problem is to maximize the user type diversity subject to limited task budget. This paper uses three heuristic algorithms to try to resolve this problem, so as to maximize user type diversity. Through extensive evaluation, the proposed algorithm Unit Reward-based Greedy Algorithm by Type obviously improves the user type diversity under different user type distributions.

众包雇用大量用户来执行某些任务，其中任务分配是一个具有挑战性的问题。现有的任务分配研究主要考虑时空多样性和能力多样性，而没有关注用户的类型多样性，这可能导致任务质量低下。本文对众包中一个新颖的任务分配问题进行了形式化处理，该任务需要各种类型的用户的协作，并且任务的质量与各种类型的招募用户高度相关。因此，问题的目标是在有限的任务预算下最大化用户类型的多样性。本文使用三种启发式算法来尝试解决此问题，从而最大程度地提高用户类型的多样性。通过广泛的评估，