User authority ranking models for community question answering
Author(s)
Xie, Haoran
Wang, Philips Fu Lee
Wong, Tak Lam
Author(s)
Rao, Y.
Liu, X.
Li, Q.
Date Issued
2016
Publisher
IOS Press
Journal
Journal of Intelligent & Fuzzy Systems
Volume
31
Issue
5
Start page
2533
End page
2542
Abstract
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.
SFU Affiliated Publication
Yes
Availability at SFU Library
No database links found.