Expert Finding for Community-Based Question Answering via Ranking Metric Network Learning

Expert finding for question answering is a challenging problem in Community-based Question Answering (CQA) site, arising in many applications such as question routing and the identification of best answers. In order to provide high-quality experts, many existing approaches learn the user model mainly from their past question-answering activities in CQA sites, which suffer from the sparsity problem of CQA data. In this paper, we consider the problem of expert finding from the viewpoint of learning ranking metric embedding. We propose a novel ranking metric network learning framework for expert finding by exploiting both users’ relative quality rank to given questions and their social relations. We then develop a randomwalk based learning method with recurrent neural networks for ranking metric network embedding. The extensive experiments on a large-scale dataset from a real world CQA site show that our method achieves better performance than other state-of-theart solutions to the problem.