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	CQARank: Jointly model topics and expertise in Community Question Answering
	Yang, Liu ; Qiu, Minghui ; Gottipati, Swapna ; Zhu, Feida ; Jiang, Jing ; Sun, Huiping ; Chen, Zhong
	2013
英文摘要	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. Copyright is held by the owner/author(s).; EI; 0
语种	英语
DOI标识	10.1145/2505515.2505720
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/325775]
专题	软件与微电子学院
推荐引用方式 GB/T 7714	Yang, Liu,Qiu, Minghui,Gottipati, Swapna,et al. CQARank: Jointly model topics and expertise in Community Question Answering. 2013-01-01.

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