| Abstract: | The problem of academic expert finding is concerned with finding the experts on a named research field. It has many real-world
applications and has recently attracted much attention. However, the existing methods are not versatile and suitable for the
special needs from academic areas where the co-authorship and the citation relation play important roles in judging researchers’
achievements. In this paper, we propose and develop a flexible data schema and a topic-sensitive co-pagerank algorithmcombined
with a topic model for solving this problem. The main idea is to measure the authors’ authorities by considering topic bias
based on their social networks and citation networks, and then, recommending expert candidates for the questions. To infer
the association between authors and topics, we draw a probability model from the latent Dirichlet allocation (LDA) model.
We further propose several techniques such as reasoning the interested topics of the query and integrating ranking metrics
to order the practices. Our experiments show that the proposed strategies are all effective to improve the retrieval accuracy. |
