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Supervised Machine Learning for Matchmaking in Digital Business Ecosystems and Platforms

Abstract : In the digital era, organizations belonging to the same or different market segments come together in digital platforms that allow them to exchange. These organizations are unified within a Digital Business Ecosystem. However, the rapid growth of the number of these organizations accentuates the complexity of finding economic partners, customers, suppliers, or other organizations that can share economic interests. In our research, we propose a recommendation system that is implemented on such a digital platform, and which is based on matchmaking and hybrid supervised machine learning algorithms. In this paper, we provide a detailed analysis of the functioning of this system, the challenge encountered when processing the data which made it possible to highlight the similarities between the organizations that can be associated. Thus, we seek to improve the understanding and analysis of the data for the identification of partners in an optimal way.
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Contributor : Mustapha Kamal Benramdane Connect in order to contact the contributor
Submitted on : Wednesday, November 9, 2022 - 12:38:36 PM
Last modification on : Friday, November 11, 2022 - 4:07:29 AM


  • HAL Id : hal-03845198, version 1



Mustapha Kamal Benramdane, Elena Kornyshova, Samia Bouzefrane, Hubert Maupas. Supervised Machine Learning for Matchmaking in Digital Business Ecosystems and Platforms. Information Systems Frontiers, In press. ⟨hal-03845198⟩



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