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Cognition-based Task Routing: Towards Highly-Effective Task-Assignments in Crowdsourcing Settings

机译:基于认知的任务路由:在众包中的高效任务分配

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In recent years the rising popularity of outsourcing work to crowds has led to increasing importance to find an effective assignment of suitable workers with tasks. Even though attempts have been made in related areas such as expertise identification most crowdsocuring jobs today are assigned without any predefined policy. Whilst some have investigated assigning jobs based on availability or experience no dominant method has been identified so far. We propose an assignment of tasks to crowd-workers based on their cognitive capability, by conducting a set of cognitive tests and comparing them with performance on typical crowdtasks. Moreover, we examine different setups to predict task performance where a) cognitive abilities, b) performance on previous crowdtasks, or c) both of them, are partially known. Preliminary results show that cognition-based task assignment leads to an improvement in task performance prediction and may pave the way to more intelligent crowd-worker recruitment.
机译:近年来,外包工作对人群的普及程度越来越重要,越来越重要,找到有效分配合适的工作人员。尽管在相关领域进行了诸如专门知识的销量,但今天大多数人为潜在的工作都没有任何预定义政策。虽然有些人根据可用性或经验进行了调查的职位,但到目前为止没有确定任何主要方法。我们通过开展一系列认知测试并将其与典型的人群的性能进行比较,提出对人群工人的任务分配给人群工人。此外,我们研究不同的设置来预测任务性能,其中A)认知能力B)对它们中的先前众人的性能,或者是部分地知道。初步结果表明,基于认知的任务任务导致任务性能预测的改进,并可能为更聪明的人群招聘铺平道路。

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