Abstract
Community question answering (CQA) sites provide us online platforms to post questions or answers. Generally, there are a great number of questions waiting to be answered by expert users. However, most of answerers are ordinary with just basic background knowledge in certain areas. To help askers to get their preferable answers, a set of possible expert users should be recommended. There have been some studies on the expert recommendation in CQA, the latest work models the user expertise under topics, where each topic is learnt based on the content and tags of questions and answers. Practically, such topics are too general, whereas question tags can be more informative and valuable than the topic of each question. In this paper, we study the user expertise under tags. Experimental analysis on a large data set from Stack Overflow demonstrates that our method performs better than the up-to-date method.