Combining social network and semantic concept analysis for personalized academic researcher recommendation

The rapid proliferation of information technologies especially Web 2.0 techniques has 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 researchers and research information on the Web has led to the problem of information overload. There is a pressing need to develop researcher recommendation agents such that users can be provided with personalized recommendations of the researchers they can potentially collaborate with for mutual research benefits. In academic contexts, recommending suitable research partners to researchers can facilitate knowledge discovery and exchange, and ultimately improve the research productivity of researchers. Existing expertise recommendation research usually investigates the expert recommending problem from two independent dimensions, namely, their social relations and expertise information. The main contribution of this paper is that we propose a network based researcher recommendation approach which combines social network analysis and semantic concept analysis in a unified framework to improve the effectiveness of personalized researcher recommendation. The results of our experiment show that the proposed approach significantly outperforms the other baseline methods. Moreover, how our proposed framework can be applied to the real-world academic contexts is explained based on a case study.

► We propose a two-layer network based approach for researcher recommendation.
► This approach combines social network analysis and semantic concept analysis in a unified framework.
► The proposed approach outperforms other two baseline approaches in the experiment.
► A case study illustrates the application of the proposed approach to the real-world academic contexts.