Conference Proceeding Article
Community Question Answering (CQA) websites, where people share expertise on open platforms, have become large repositories of valuable knowledge. To bring the best value out of these knowledge repositories, it is critically important for CQA services to know how to find the right experts, retrieve archived similar questions and recommend best answers to new questions. To tackle this cluster of closely related problems in a principled approach, we proposed Topic Expertise Model (TEM), a novel probabilistic generative model with GMM hybrid, to jointly model topics and expertise by integrating textual content model and link structure analysis. Based on TEM results, we proposed CQARank to measure user interests and expertise score under different topics. Leveraging the question answering history based on long-term community reviews and voting, our method could find experts with both similar topical preference and high topical expertise. Experiments carried out on Stack Overflow data, the largest CQA focused on computer programming, show that our method achieves significant improvement over existing methods on multiple metrics.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
CIKM'13: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management: October 27- November 1, 2013, San Francisco, CA
City or Country
YANG, Liu; QIU, Minghui; GOTTOPATI, Swapna; ZHU, Feida; JIANG, Jing; SUN, Huiping; and CHEN, Zhong.
CQARank: Jointly Model Topics and Expertise in Community Question Answering. (2013). CIKM'13: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management: October 27- November 1, 2013, San Francisco, CA. 99-108. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2232
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