Scientific Matchmaker: Collaborator Recommender System
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.
KeywordsRecommender systems Co-authorship network Scientific collaboration
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.
- 7.Yan, E., Ding, Y.: Applying centrality measures to impact analysis: a coauthorship network analysis. J. IST Assoc. 60(10), 2107–2118 (2009)Google Scholar
- 9.Velden, T., Lagoze, C.: Patterns of collaboration in co-authorship networks in chemistry-mesoscopic analysis and interpretation. In: ISSI 2009 (2009)Google Scholar
- 11.Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 448–456. ACM (2011)Google Scholar
- 12.Mimno, D., McCallum, A.: Expertise modeling for matching papers with reviewers. In: Proceedings of the 13th ACM SIGKDD IC, pp. 500–509 (2007)Google Scholar
- 13.Makarov, I., Bulanov, O., Zhukov, L.: Co-author recommender system. In: Kalyagin, V., Nikolaev, A., Pardalos, P., Prokopyev, O. (eds.) Springer Proceedings in Mathematics and Statistic, vol. 197, pp. 1–6. Springer, Cham (2017)Google Scholar
- 14.Powered by HSE Portal: Publications of HSE (2017). http://publications.hse.ru/en. Accessed 9 May 2017
- 17.BigARTM contributors: BigARTM v0.8.2, December 2016. https://doi.org/10.5281/zenodo.288960
- 18.Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. IST Assoc. 58(7), 1019–1031 (2007)Google Scholar
- 21.Wainwright, M.J., Ravikumar, P., Lafferty, J.D.: High-dimensional graphical model selection using \(l_1\)-regularized logistic regression. Adv. Neural Inf. Process. Syst. 19, 1465 (2007)Google Scholar
- 22.Beel, J., et al.: Research paper recommender system evaluation: a quantitative literature survey. In: Proceedings of the International Workshop on RepSys 2013, pp. 15–22. ACM, New York (2013)Google Scholar