中文简单介绍： 本文对怎样在问答社区对用户主题兴趣及专业度建模分析进行了研究，而且提出了针对此问题的统计图模型Topics Expertise Model.
论文出处：CIKM‘13.
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.
下载链接：http://dl.acm.org/citation.cfm?id=2505720 http://www.mysmu.edu/faculty/fdzhu/paper/CIKM'13.pdf
开源code链接：https://github.com/yangliuy/TopicExpertiseModel