The rapid increase in the number of community-based question-and-answer services is attracting many users. Questions are posted and answered by community members. These users, who can help other users answer questions, can be considered experts. To facilitate finding a suitable expert and alleviate information overload, in this paper, expert yellow pages (EYP) for community question answering (CQA) are constructed. Considering the various lengths of texts, the biterm topic model (BTM) is used to model questions and fields of expertise. Then, two-dimensional EYP (2DEYP), which are composed of expertise field dimensions and question dimensions, are constructed. The intersections represent the cluster of experts. The proposed 2DEYP can be expanded both laterally and vertically for a more in-depth understanding and a more precise location. As the closer neurons represent similar topics, a novel labelling method is proposed to identify topic words for navigation. The method uses the distance between neurons as the differentiation capability. To further distinguish experts, a ranking mechanism is proposed. The experts can be ranked by integrating their expertise and activity levels. The expertise level is novel and characterized by both breadth and depth aspects. The proposed approach is evaluated via a real dataset, and the experimental results show that the proposed algorithm is feasible and performs well.

中文翻译：

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一种为社区问题解答站点构建专家黄页的方法

基于社区的问答服务的数量迅速增加，吸引了许多用户。问题由社区成员发布并回答。这些可以帮助其他用户回答问题的用户可以被视为专家。为了方便找到合适的专家并减轻信息过多，本文构建了用于社区问题解答（CQA）的专家黄页（EYP）。考虑到文本的不同长度，使用了双项主题模型（BTM）来对问题和专业领域进行建模。然后，构建了由专业领域维度和问题维度组成的二维EYP（2DEYP）。相交代表专家群。拟议的2DEYP可以横向和纵向扩展，以更深入地了解和更精确地定位。由于较近的神经元代表相似的主题，因此提出了一种新颖的标记方法来标识要导航的主题词。该方法使用神经元之间的距离作为分化能力。为了进一步区分专家，提出了一种排名机制。可以通过整合他们的专业知识和活动水平来对专家进行排名。专业知识水平是新颖的，具有广度和深度两个方面的特征。通过真实数据集对所提方法进行了评估，实验结果表明所提算法是可行的，并且性能良好。可以通过整合他们的专业知识和活动水平来对专家进行排名。专业知识水平是新颖的，具有广度和深度两个方面的特征。通过真实数据集对所提方法进行了评估，实验结果表明所提算法是可行的，并且性能良好。可以通过整合他们的专业知识和活动水平来对专家进行排名。专业知识水平是新颖的，具有广度和深度两个方面的特征。通过真实数据集对所提方法进行了评估，实验结果表明所提算法是可行的，并且性能良好。