A Multilevel Information Mining Approach for Expert Recommendation in Online Scientific Communities

摘要

Expert recommendation plays a vital role in the expansion of researchers' academic communities and in the creation of potential collaboration opportunities. Current approaches for academic expert recommendation are mainly based on keywords-based research relevance and social network proximity between researchers. However, most proximity measures only focus on the individual level in the network. Therefore, we develop a new measure for the proximity at the institutional level that measures the link strength between researchers' affiliated institutions. Moreover, a multilevel profile-based approach is proposed to identify the most suitable expert for research collaboration by integrating research relevance information, individual social network information and institutional connectivity information. The proposed approach has been implemented in ScholarMate, which is a research 2.0 innovation, promoting knowledge-sharing activities in the virtual scientific community. According to the results of the experiments conducted on the real-world dataset, institutional connectivity is proved to be an important factor for expert recommendation and the proposed hybrid method outperforms all the other benchmark algorithms significantly.

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