Routing questions for collaborative answering in Community Question Answering

Shuo Chang, Aditya Pal
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引用次数: 93

Abstract

Community Question Answering (CQA) service enables its users to exchange knowledge in the form of questions and answers. By allowing the users to contribute knowledge, CQA not only satisfies the question askers but also provides valuable references to other users with similar queries. Due to a large volume of questions, not all questions get fully answered. As a result, it can be useful to route a question to a potential answerer. In this paper, we present a question routing scheme which takes into account the answering, commenting and voting propensities of the users. Unlike prior work which focuses on routing a question to the most desirable expert, we focus on routing it to a group of users - who would be willing to collaborate and provide useful answers to that question. Through empirical evidence, we show that more answers and comments are desirable for improving the lasting value of a question-answer thread. As a result, our focus is on routing a question to a team of compatible users.We propose a recommendation model that takes into account the compatibility, topical expertise and availability of the users. Our experiments over a large real-world dataset shows the effectiveness of our approach over several baseline models.
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在社区问答中为协作回答路由问题
社区问答(CQA)服务使用户能够以问答的形式交换知识。通过允许用户贡献知识,CQA不仅满足了提问者,还为其他有类似查询的用户提供了有价值的参考。由于有大量的问题,并不是所有的问题都得到了充分的回答。因此,将问题传递给潜在的答案是很有用的。本文提出了一种考虑用户回答、评论和投票倾向的问题路由方案。与之前的工作不同,我们专注于将问题路由给最理想的专家,我们专注于将问题路由给一组用户——他们愿意合作并为该问题提供有用的答案。通过实证研究,我们发现更多的回答和评论对于提高问答主题的持久价值是可取的。因此,我们的重点是将问题发送给兼容的用户团队。我们提出了一个考虑兼容性、专题专业知识和用户可用性的推荐模型。我们在一个大型真实数据集上的实验表明,我们的方法在几个基线模型上是有效的。
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