Topic Expertise Model

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.
This package implements Gibbs sampling for Topic Expertise Model for jointly modeling topics and expertise in question answering communities. More details of our model are described in the related publication http://dl.acm.org/citation.cfm?id=2505720.Related Publication: Yang, L., Qiu, M., Gottipati, S., Zhu, F., Jiang, J., Sun, H., & Chen, Z. (2013). CQArank: jointly model topics and expertise in community question answering. Paper presented at the 22nd ACM International Conference on Information & Knowledge Management, San Francisco, California, USA.
DOI: 10.1145/2505515.2505720

社区问答网站（CQA），其中人们在大平台上分享专业知识，已经成为了宝贵知识的庞大宝库。为了从这些知识库中提取最大价值，对于CQA服务来说，了解如何找到合适的专家、检索存档的相似问题和为新问题推荐最佳答案显得至关重要。为了以原则性的方法解决这一系列紧密相关的问题，我们提出了主题专业知识模型（TEM），这是一种结合了高斯混合模型（GMM）的混合概率生成模型，通过整合文本内容模型和链接结构分析来联合建模主题和专业知识。基于TEM的结果，我们提出了CQARank，用于在不同主题下衡量用户兴趣和专业评分。利用基于长期社区评论和投票的问答历史，我们的方法能够找到具有相似主题偏好和高度主题专业知识的专家。此包实现了针对问答社区中主题和专业知识的联合建模的主题专业知识模型（TEM）的Gibbs抽样。关于我们模型的更多细节，请参阅相关出版物http://dl.acm.org/citation.cfm?id=2505720。相关出版物：Yang, L., Qiu, M., Gottipati, S., Zhu, F., Jiang, J., Sun, H., & Chen, Z. (2013). CQArank: 在社区问答中联合建模主题和专业知识。论文发表于第22届ACM国际信息与知识管理会议，美国加州旧金山。DOI: 10.1145/2505515.2505720。