使用异质工作者对一组对象进行排序：QUITE——一个简单的问题
Ranking a Set of Objects using Heterogeneous Workers: QUITE an Easy Problem
AI总结：
针对异质可靠度众包工作者提供的含噪成对比较下的对象排序问题，提出可联合估计工作者可靠度与对象质量的非自适应排序算法QUITE，验证其性能并给出自适应改造方法。
摘要：
我们聚焦于对N个对象进行排序的问题，排序的起始输入是由能力不均的众包工作者提供的一组含噪声的成对比较结果，每个工作者都有特定的可靠度，该可靠度反映了其对对象对进行排序的能力。更具体地说，我们假设对象具有内在质量，且一个对象被偏好于另一个对象的概率，既取决于两个比较对象的质量差，也取决于工作者的可靠度。我们提出了QUITE，这是一种非自适应排序算法，可联合估计工作者的可靠度和对象的质量。我们在不同场景下将QUITE的性能与此前提出的算法进行了对比。最后，我们展示了如何自然地将QUITE改造为自适应算法。
英文摘要：
We focus on the problem of ranking $N$ objects starting from a set of noisy pairwise comparisons provided by a crowd of unequal workers, each worker being characterized by a specific degree of reliability, which reflects her ability to rank pairs of objects. More specifically, we assume that objects are endowed with intrinsic qualities and that the probability with which an object is preferred to another depends both on the difference between the qualities of the two competitors and on the reliability of the worker. We propose QUITE, a non-adaptive ranking algorithm that jointly estimates workers' reliabilities and qualities of objects. Performance of QUITE is compared in different scenarios against previously proposed algorithms. Finally, we show how QUITE can be naturally made adaptive.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
