User authority ranking models for community question answering

Yanghui RAO, Haoran XIE, Xuebo LIU, Qing LI, Fu Lee WANG, Tak Lam WONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

1 Citation (Scopus)


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
Issue number5
Publication statusPublished - 13 Oct 2016
Externally publishedYes


  • User authority ranking
  • community question answering
  • topic modeling


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