Community Question Answering (CQA) websites are widely used in sharing knowledge, where users can ask questions, reply answers and evaluate answers. So far, the evaluation of answers has been explained by the contents of answers through the investigation of users’ topics of interest and expertise levels. In this paper we focus on modeling the user’s evaluation behavior, in that users can see the answerer’s profile as well as the answer content before evaluating the quality of the answer. We propose a model called Popularity-based Topical Expertise Model (PTEM), a generative model to analyze the rich-get-richer phenomenon that popular user’s answers are more recommended. We can simultaneously estimate the topical expertise of each user and the strength of the rich-get-richer effect through the EM algorithm combined with collapsed Gibbs sampling. Experiments are performed on the StackExchange data, and the results demonstrate a rich-get-richer phenomenon in the community. We further discuss the superiority and usefulness of the proposed model through analysis in the discipline of philosophy.

中文翻译：

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PTEM：基于流行度的主题专业知识模型，用于社区问答

社区问题解答（CQA）网站被广泛用于共享知识，用户可以在其中提问，答复和评估答案。到目前为止，通过对用户感兴趣的主题和专业水平的调查，答案的内容已解释为答案的解释。在本文中，我们着重于对用户的评估行为进行建模，因为用户可以在评估答案的质量之前查看答复者的个人资料以及答案的内容。我们提出了一个名为“基于流行度的主题专长模型”（PTEM）的模型，该模型用于分析“富人越富”现象，即更推荐流行用户的答案。通过结合折叠的吉布斯抽样的EM算法，我们可以同时估算每个用户的主题专业知识和丰富效果的强度。对StackExchange数据进行了实验，结果证明了社区中越来越丰富的现象。通过对哲学学科的分析，我们进一步讨论了该模型的优越性和实用性。