期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Collusion-Proof Result Inference in Crowdsourcing 被引量:3
1
作者 Peng-Peng Chen Hai-Long Sun +1 位作者 Yi-Li Fang Jin-Peng Huai 《Journal of Computer Science & Technology》 SCIE EI CSCD 2018年第2期351-365,共15页
In traditional crowdsourcing, workers are expected to provide independent answers to tasks so as to ensure the diversity of answers. However, recent studies show that the crowd is not a collection of independent worke... In traditional crowdsourcing, workers are expected to provide independent answers to tasks so as to ensure the diversity of answers. However, recent studies show that the crowd is not a collection of independent workers, but instead that workers communicate and collaborate with each other. To pursue more rewards with little effort, some workers may collude to provide repeated answers, which will damage the quality of the aggregated results. Nonetheless, there are few efforts considering the negative impact of collusion on result inference in crowdsourcing. In this paper, we are specially concerned with the Collusion-Proof result inference problem for general crowdsourcing tasks in public platforms. To that end, we design a metric, the worker performance change rate, to identify the colluded answers by computing the difference of the mean worker performance before and after removing the repeated answers. Then we incorporate the collusion detection result into existing result inference methods to guarantee the quality of the aggregated results even with the occurrence of collusion behaviors. With real-world and synthetic datasets, we conducted an extensive set of evaluations of our approach. The experimental results demonstrate the superiority of our approach in comparison with the state-of-the-art methods. 展开更多
关键词 crowdsourcing quality control COLLUSION collaborative crowdsourcing result inference
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部