Explicit Preference Elicitation for Task Completion Time
Explicit Preference Elicitation for Task Completion Time
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DOI：
10.1145/3269206.3271667
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
M. Esfandiari;Senjuti Basu Roy;S. Amer-Yahia
中科院分区：
文献类型：
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作者：
M. Esfandiari;Senjuti Basu Roy;S. Amer-Yahia
目前的众包平台对员工反馈提供的支持很少。有时，工作人员会被邀请发布自由文本，描述他们在完成任务时的经验和偏好。他们还可以使用 Turker Nation1 等论坛来交换任务和请求者的偏好。事实上，众包平台在很大程度上依赖于观察工人并隐含地推断他们的偏好。相反，我们相信，要求员工明确表明他们的偏好将使我们能够改进众包平台的不同流程。我们发起了一项研究，利用员工的明确启发来捕捉员工偏好的不断变化的性质，并提出了一个优化框架，以更好地理解和估计任务完成时间。我们设计了一个工人模型来估计任务完成时间，通过请求工人对任务因素（例如所需技能、任务付款和任务相关性）的偏好来迭代提高其准确性。我们开发有保证的有效解决方案，对大规模现实世界数据进行广泛的实验，显示显式偏好引发相对于具有统计显着性的隐式偏好引发的优势。
Current crowdsourcing platforms provide little support for worker feedback. Workers are sometimes invited to post free text describing their experience and preferences in completing tasks. They can also use forums such as Turker Nation1 to exchange preferences on tasks and requesters. In fact, crowdsourcing platforms rely heavily on observing workers and inferring their preferences implicitly. On the contrary, we believe that asking workers to indicate their preferences explicitly will allow us to improve different processes in crowdsourcing platforms. We initiate a study that leverages explicit elicitation from workers to capture the evolving nature of worker preferences and we propose an optimization framework to better understand and estimate task completion time. We design a Worker model to estimate task completion time whose accuracy is improved iteratively by requesting worker preferences for task factors, such as, required skills, task payment, and task relevance. We develop efficient solutions with guarantees, run extensive experiments with large-scale real-world data that show the benefit of explicit preference elicitation over implicit ones with statistical significance.