Abstract
The purpose of this paper is developing a method for recommending business and scientific partners matchmaking with use of a deep learning model based on historical data on previously completed European Union projects. The paper starts with an introduction to recommender systems, followed by a systematic literature review on the subject. The next part describes the course of the research and its implementation of two deep learning approaches: (1) the entity embeddings of organisations and (2) the embedding space of keywords. The paper ends with a summary of the entity embedding-based recommendation characterized by coverage, average accuracy, low Gini index and the entropy measure.
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The publication has been prepared as a part of the Support Programme of the Partnership between Higher Education and Science and Business Activity Sector financed by City of Wroclaw.
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Król, D., Zborowska, Z., Ropa, P., Kincel, Ł. (2022). CORDIS Partner Matching Algorithm for Recommender Systems. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13757. Springer, Cham. https://doi.org/10.1007/978-3-031-21743-2_56
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