User authority ranking models for community question answering

Y. Rao, H. Xie, X. Liu, Qing Li, F.L. Wang, T.-L. Wong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

© 2016 - IOS Press and the authors. All rights reserved.The proliferation of knowledge-sharing communities has generated large amounts of data. Prominent examples of how user-generated content can be harnessed include IBM's Watson question answering sytem and Apple's Siri, the question answering application in iPhones. Facing such massive data, user authority ranking is important to the development of question answering and other e-commerce services. In this study, we propose three probabilistic models to rank the user authority of each question. Compared to the existing approaches focused on the user relationship primarily, our method is more effective because we consider the link structure and topical similarities between users and questions simultaneously. We use a real-world dataset from Zhihu, a popular community question answering website in China to conduct experiments. Experimental results show that our model outperforms other baseline methods in ranking the user authority.
Original languageEnglish
Pages (from-to)2533-2542
Number of pages10
JournalJournal of Intelligent and Fuzzy Systems
Volume31
Issue number5
DOIs
Publication statusPublished - 13 Oct 2016
Externally publishedYes

Keywords

  • community question answering
  • topic modeling
  • User authority ranking

ASJC Scopus subject areas

  • Statistics and Probability
  • Engineering(all)
  • Artificial Intelligence

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