A classification-based approach to question routing in community question answering

Community-based Question and Answering (CQA) services have brought users to a new era of knowledge dissemination by allowing users to ask questions and to answer other users' questions. However, due to the fast increasing of posted questions and the lack of an effective way to find interesting questions, there is a serious gap between posted questions and potential answerers. This gap may degrade a CQA service's performance as well as reduce users' loyalty to the system. To bridge the gap, we present a new approach to Question Routing, which aims at routing questions to participants who are likely to provide answers. We consider the problem of question routing as a classification task, and develop a variety of local and global features which capture different aspects of questions, users, and their relations. Our experimental results obtained from an evaluation over the Yahoo!~Answers dataset demonstrate high feasibility of question routing. We also perform a systematical comparison on how different types of features contribute to the final results and show that question-user relationship features play a key role in improving the overall performance.