Predicting Best Responder in Community Question Answering Using Topic Model Method

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 the more knowledge people provided are shared in CQA, how to use the historical knowledge for solving new questions has become a crucial problem. In this paper, we investigate the problem as predicting best responders for new questions and tackle the problem from two perspectives, one is from the asker of the new question, and the other is from the question itself. We propose two supervised topic models, Asker-Responder Topic Model (ARTM) and Question-Responder Topic Model (QRTM) for both two perspectives by tracking people's answering history as background knowledge. Our experiments show that the two supervised topic models can effectively predict best responders for new questions in CQA without any additional works and have significant improvement over the baseline method.