Towards expert finding by leveraging relevant categories in authority ranking

How to improve authority ranking is a crucial research problem for expert finding. In this paper, we propose a novel framework for expert finding based on the authority information in the target category as well as the relevant categories. First, we develop a scalable method for measuring the relevancy between categories through topic models. Then, we provide a link analysis approach for ranking user authority by considering the information in both the target category and the relevant categories. Finally, the extensive experiments on two large-scale real-world Q&A data sets clearly show that the proposed method outperforms the baseline methods with a significant margin.