Abstract
Modern co-authorship networks contain hidden patterns of researchers interaction and publishing activities. We aim to provide a system for selecting a collaborator for joint research or an expert on a given list of topics. We have improved a recommender system for finding possible collaborator with respect to research interests and predicting quality and quantity of the anticipated publications. Our system is based on a co-authorship network derived from the bibliographic database, as well as content information on research papers obtained from SJR Scimago, staff information and the other features from the open data of researchers profiles. We formulate the recommendation problem as a weighted link prediction within the co-authorship network and evaluate its prediction for strong and weak ties in collaborative communities.
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Acknowledgements
The work was supported by the Russian Science Foundation under grant 17-11-01294 and performed at National Research University Higher School of Economics, Russia.
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Makarov, I., Bulanov, O., Gerasimova, O., Meshcheryakova, N., Karpov, I., Zhukov, L.E. (2018). Scientific Matchmaker: Collaborator Recommender System. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2017. Lecture Notes in Computer Science(), vol 10716. Springer, Cham. https://doi.org/10.1007/978-3-319-73013-4_37
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