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Crowdsourcing appeared on the Internet as a new service mode. In order to improve the accuracy of crowdsourcing results evaluation, it presents a crowdsourcing iterative detection strategy. According to the task crowdsourcing workers complete, we can use the principle that the minority is subordinate to the majority to assess the results of the task. The result sets of the assessment task in which option is not unique will be regarded as a new task to release in the crowdsourcing platform. Candidate workers will be chosen to participate in the iterative detection operations until the optimal result of each task has been determined. Experimental results show that compared with the classic quality assessment algorithm based on entropy, this mechanism can achieve better results. Keywords—crowdsourcing; quality control; entropy; iterative detection strategy; quality evaluation

Application Research of Iterative Detection Strategy in the Crowdsourcing Quality Evaluation