A Probabilistic Model for Fine-Grained Expert Search

Expert search, in which given a query a ranked list of experts instead of documents is returned, has been intensively studied recently due to its importance in facilitating the needs of both information access and knowledge discovery. Many approaches have been pro-posed, including metadata extraction, expert profile building, and formal model generation. However, all of them conduct expert search with a coarse-grained approach. With these, further improvements on expert search are hard to achieve. In this paper, we propose conducting expert search with a fine-grained approach. Specifically, we utilize more spe-cific evidences existing in the documents. An evidence-oriented probabilistic model for ex-pert search and a method for the implementa-tion are proposed. Experimental results show that the proposed model and the implementa-tion are highly effective.