基于迁移学习的twitter人群选择框架

Zhou Zhao, D. Yan, Wilfred Ng, Shi Gao
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引用次数: 32

摘要

人群选择对于众包应用程序至关重要,因为选择具有特定专业知识的合适员工来执行众包任务非常重要。核心问题很简单但很棘手:给定一个众包任务,谁是最有知识的用户?在这个演示中,我们展示了我们的框架,它可以根据Twitter用户的社交活动来处理Twitter上的众包任务分配问题。由于Twitter上的用户资料不会显示用户的兴趣和技能,我们将从雅虎分类中转移这些信息。回答用于学习用户专业知识的数据集。然后,我们根据用户的专业知识为某些任务选择合适的人群。我们通过广泛的用户评估来研究我们系统的有效性。我们进一步鼓励与会者参与一个名为“在Twitter上问谁”的游戏。这有助于以互动的方式理解我们的想法。我们的人群选择可以通过以下url http://webproject2.cse.ust.hk:8034/tcrowd/访问。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A transfer learning based framework of crowd-selection on twitter
Crowd selection is essential to crowd sourcing applications, since choosing the right workers with particular expertise to carry out crowdsourced tasks is extremely important. The central problem is simple but tricky: given a crowdsourced task, who are the most knowledgable users to ask? In this demo, we show our framework that tackles the problem of crowdsourced task assignment on Twitter according to the social activities of its users. Since user profiles on Twitter do not reveal user interests and skills, we transfer the knowledge from categorized Yahoo! Answers datasets for learning user expertise. Then, we select the right crowd for certain tasks based on user expertise. We study the effectiveness of our system using extensive user evaluation. We further engage the attendees to participate a game called--Whom to Ask on Twitter?. This helps understand our ideas in an interactive manner. Our crowd selection can be accessed by the following url http://webproject2.cse.ust.hk:8034/tcrowd/.
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