Crowdsourcing harnesses human effort to solve computer-hard problems such as photo tagging, entity resolution and sentiment analysis. Such tasks often have different levels of difficulty and workers have varying levels of skill at completing them. With a limited budget, it is important to wisely allocate the spend among the tasks and workers such that the overall outcome is as good as possible. Most existing work addresses this budget allocation problem by assuming that workers have a single level of ability for all tasks and each task involves a choice between just two alternatives. However, this neglects the fact that many crowdsourcing applications ask workers to choose between multiple alternatives and that different tasks can belong to a variety of diverse categories. Moreover, workers may have varying abilities across these categories. For example, a science enthusiast is likely to do better than a cinephile when answering a question such as “selecting the melting point of Copper from 1) 327 degrees Celcius, 2) 1085 degrees Celcius and 3) 1495 degrees Celcius”. And a cinephile is likely to perform better in tasks related to movies such as “how many episodes of Fooly Cooly were ever made? 1) 6 2) 7 and 3) 8”. To incorporate such category-aware crowdsourcing of multiple-choice tasks, we model the interaction between the crowdsource campaign initiator and the workers as a procurement auction and propose a computationally efficient mechanism, INCARE, to achieve high-quality outcomes given a limited budget. We prove that INCARE is budget feasible, incentive compatible and individually rational. We also prove that INCARE can achieve a bounded approximation ratio for the optimal budget allocation mechanism with full knowledge of workers' true costs. Finally, our numerical simulations, on both real and synthetic data, show that, compared to the state of the art, INCARE: (i) can improve the accuracy by up to 98%, given a limited budget; and (ii) is significantly more robust to inaccuracies in prior information about each worker's ability and each task's difficulty.

众包利用人力来解决计算机难题，例如照片标记、实体解析和情感分析。此类任务通常具有不同程度的难度，并且工人在完成这些任务时具有不同的技能水平。在预算有限的情况下，明智地在任务和工作人员之间分配支出非常重要，这样总体结果就会尽可能好。大多数现有工作通过假设工人对所有任务具有单一能力水平并且每项任务只涉及在两个选项之间进行选择来解决这个预算分配问题。然而，这忽略了一个事实，即许多众包应用程序要求工作人员在多种选择之间进行选择，并且不同的任务可以属于各种不同的类别。此外，工人在这些类别中可能具有不同的能力。例如，在回答诸如“从 1) 327 摄氏度、2) 1085 摄氏度和 3) 1495 摄氏度中选择铜的熔点”之类的问题时，科学爱好者可能比影迷做得更好。一个影迷可能会在与电影相关的任务中表现得更好，比如“愚蠢的库利制作了多少集？1) 6 2) 7 和 3) 8”。为了将此类多选任务的类别感知众包结合起来，我们将众包活动发起者与工作人员之间的交互建模为采购拍卖，并提出了一种计算效率高的机制 INCARE，以在有限的预算下实现高质量的结果。我们证明 INCARE 是预算可行的、激励兼容的和个人理性的。我们还证明，在充分了解工人真实成本的情况下，INCARE 可以实现最优预算分配机制的有界近似比率。最后，我们对真实数据和合成数据的数值模拟表明，与现有技术相比，INCARE：(i) 在预算有限的情况下，可以将准确度提高多达 98%；(ii) 对关于每个工人的能力和每项任务的难度的先验信息的不准确性明显更加稳健。