Business Recommender System through Matchmaking with Supervised Machine Learning in Distributed Digital Platforms: Energy Complexity Analysis

Digital platforms bring together organizations from different market segments. Consequently, digital business ecosystems orient themselves gradually according to the constraints imposed by different organizations although they are under the same segments. This phenomenon of influence also considerably enriches the data corpus. It has seldom been observed that the existing data features are always dynamic in nature. The context has become more challenging as many companies are often reluctant to share their information probably due to its confidentiality. Hence, with this paradigm of several variations, conventional matching to search a particular enterprise from the largest data corpus fails to deliver optimal matching prediction with respect to the different roles of the enterprises. This article presents an analytical and practical case study deploying a hybrid machine learning algorithm. The proposed methods depict the background of the digital business ecosystem, missing data imputation, and supervised machine learning approaches towards developing such models.

show abstract

Business Recommender System through Matchmaking with Supervised Machine Learning in Distributed Digital Platforms: Energy Complexity Analysis

Cited by 2 publications

References 16 publications

Towards an Energy Complexity Model for Distributed Data Processing Algorithms

Towards an Energy Complexity Model for Distributed Data Processing Algorithms

Hybrid Machine Learning for Matchmaking in Digital Business Ecosystems

Contact Info

Product

Resources

About