A personalized researcher recommendation approach in academic contexts: Combining social networks and semantic concepts analysis

The rapid proliferation of information technologies especially the web 2.0 techniques have changed the fundamental ways how things can be done in many areas, including how researchers could communicate and collaborate with each other. The presence of the sheer volume of researcher and topical research information on the Web has led to the problem of information overload. There is a pressing need to develop researcher recommender systems such that users can be provided with personalized recommendations of the researchers they can potentially collaborate with for mutual research benefits. In an academic context, recommending suitable research partners to researchers can facilitate knowledge discovery and exchange, and ultimately improve the research productivity of both sides. Existing expertise recommendation research usually investigates into the expert finding problem from two independent dimensions, namely, the social relations and the common expertise. The main contribution of this paper is that we propose a novel researcher recommendation approach which combines the two dimensions of social relations and common expertise in a unified framework to improve the effectiveness of personalized researcher recommendation. Moreover, how our proposed framework can be applied to the real-world academic contexts is explained based on two case studies.