Expert finding in question-answering websites: a novel hybrid approach
Expert finding in question-answering websites: a novel hybrid approach
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DOI：
10.1145/1774088.1774266
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发表时间：
2010-03
期刊：
影响因子：
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通讯作者：
Wei-Chen Kao;Duen-Ren Liu;Shi Wang
中科院分区：
文献类型：
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作者：
Wei-Chen Kao;Duen-Ren Liu;Shi Wang
问答网站正在成为一个越来越受欢迎的知识共享平台。在这样的网站上，人们可以提出任何类型的问题，然后等待其他人回答问题。然而，在这种情况下，提问者可能无法从适当的专家那里获得正确的答案，并且通过问答网站进行的知识共享受到干扰。最近，已经提出了各种方法来自动查找问答网站中的专家。在本文中，我们提出了一种新的混合方法来有效地为问答网站中的目标问题类别寻找专家。我们的方法在寻找专家时考虑了用户主题相关性、用户口碑和类别的权威性。实验结果表明，本文提出的方法优于其他传统方法。
Question answering websites are becoming an ever more popular knowledge sharing platform. On such websites, people may ask any type of question and then wait for someone else to answer the question. However, in this manner, askers may not obtain correct answers from appropriate experts, and knowledge sharing through question answering websites is interfered. Recently, various approaches have been proposed to automatically find experts in Question answering websites. In this paper, we propose a novel hybrid approach to effectively find experts for the category of the target question in question answering websites. Our approach considers user subject relevance, user reputation and authority of a category in finding experts. The experiment results show that our proposed methods outperform other conventional methods.