Exploring user expertise and descriptive ability in community question answering

Abstract

The research on community question answering (CQA) has been paid increasing attention in recent years. In CQA, to reduce the number of unanswered questions and the time for askers to wait, it is very necessary to identify relevant experts or best answers for these questions. Generally, the experts' answers are more likely to be the best answers. Existing studies considered that user expertise is reflected by the voting scores of both answers and questions. However, voting scores of questions are not really related to user expertise. In this paper, we proposed a new probabilistic model to depict users' expertise based on answers and their descriptive ability based on questions. To exploit social information in CQA, the link analysis is also considered. Extensive experiments on the large Stack Overflow dataset demonstrate that our methods can achieve comparable or even better performance than the state-of-the-art models.