Holistic Crowd-Powered Sorting via AID: Optimizing for Accuracies, Inconsistencies, and Difficulties
Holistic Crowd-Powered Sorting via AID: Optimizing for Accuracies, Inconsistencies, and Difficulties
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通过 AID 进行整体群体支持的排序：针对准确性、不一致和困难进行优化
DOI：
10.1145/3269206.3269279
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Parameswaran, Aditya
中科院分区：
文献类型：
--
作者：
Rajpal, Shreya;Parameswaran, Aditya
我们重新审视使用众包成对比较对对象进行排序的基本问题。以前的工作要么把这些比较作为独立的任务-在这种情况下，最终得出的判断可能是不一致的，或者未能捕捉工人的准确性或成对比较的困难-在这种情况下，最终得出的判断可能是一致的，但最终更不准确。我们采用了一个整体的方法，构建了一个图的对象尊重一致性约束。我们的主要贡献是一种新的方法，编码困难的比较的形式限制边缘。我们将其与迭代E-M风格的过程相结合，以揭示有关潜在变量和约束的信息沿着图结构。我们表明，我们的方法预测的边缘方向以及难度值更准确地比基线的真实的和模拟数据，在各种大小的图形。
We revisit the fundamental problem of sorting objects using crowdsourced pairwise comparisons. Prior work either treats these comparisons as independent tasks -- in which case the resulting judgments may end up being inconsistent, or fails to capture the accuracies of workers or difficulties of the pairwise comparisons -- in which case the resulting judgments may end up being consistent with each other, but ultimately more inaccurate. We adopt a holistic approach that constructs a graph across the set of objects respecting consistency constraints. Our key contribution is a novel method of encoding difficulty of comparisons in the form of constraints on edges. We couple that with an iterative E-M-style procedure to uncover information about latent variables and constraints, along with the graph structure. We show that our approach predicts edge directions as well as difficulty values more accurately than baselines on both real and simulated data, across graphs of various sizes.
众包环境中的动态最大算法
DOI：
10.1145/2339530.2339707
发表时间：
2012
期刊：
Proceedings of the 25th annual ACM symposium on User interface software and technology
影响因子：
--
作者：
Petros Venetis;H. Garcia
通讯作者：
H. Garcia