Affiliations: [a]
School of Data and Computer Science, Sun Yat-sen University, China
| [b]
Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong | [c]
Department of Computer Science, City University of Hong Kong, Hong Kong
| [d]
Caritas Institute of Higher Education, Hong Kong
Correspondence:
[*]
Corresponding author. Haoran Xie, Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong. Tel.: +852 29488545; Fax: +852 29487726; E-mail: [email protected].
Note: [1] This paper is an extension of work originally published in Proceedings of the 14th International Conference on Web-based Learning (ICWL 2015) [1].
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.
Keywords: User authority ranking, topic modeling, community question answering
