Topic-Level Expert Modeling in Community Question Answering

Community Question Answering (CQA) services provide an open platform for people to share their knowledge and have attracted great attention for its rapidly increasing popularity. As more knowledge is shared in CQA, how to use the repository for solving new questions has become a crucial problem. In this paper, we tackle the problem by finding experts from the question answering history first and then recommending the appropriate experts to answer the new questions. We develop the Topic-level Expert Learning (TEL) model to find experts on topic level in CQA. Our proposed model incorporates link analysis into content analysis. The main difference between TEL and other generative models is that TEL can automatically adjust and update the sampling parameters during iterations in order to better model the experts on topic level. The experiments are conducted on datasets crawled from Yahoo! Answers and the results show that our method can effectively find experts to answer new questions and can better predict best responders for new questions. Our model achieves significant improvement over the baseline methods on multiple metrics. Beyond these metric performances, TEL converges fast within a few iterations.