Recommendation Service for Contractor Search Based on Machine Learning

In the rapidly evolving and increasingly competitive modern market, the importance of building reliable B2B relationships is on the rise. There emerges a need for optimization of the processes for searching and selecting trustworthy counterparts. A recommendation service based on machine learning addresses this challenge by offering an automated system for the search and evaluation of potential business partners. The relevance of such service development is due to several factors. Firstly, there is a significant increase in the volume of data that must be analyzed for decision-making regarding collaboration. Secondly, there is a need to enhance the speed and quality of decision-making to maintain and strengthen market positions. Thirdly, the complexity of verifying the reliability of counterparts and assessing their potential benefits for business is ever-growing. Machine learning has demonstrated its efficiency in large data analysis and outcome prediction with high accuracy. A recommendation service based on it could apply a variety of methods to forecast the potential profitability of relationships with new counterparts. Implementing such a system will lead to numerous positive changes in the business partner search process. First and foremost, it will allow companies to significantly reduce the time and costs associated with searching and vetting counterparts. Additionally, the precision of selecting potentially beneficial and reliable partnerships will increase, which may in turn lead to higher company revenues and reduced commercial risks. Key Words: recommendation service, machine learning, B2B relationships, counterparty search, data analysis, business process optimization, counterparty verification, risk management, decision automation. This work consists of 45 pages, 3 chapters, 9 images, 18 sources, and 1 appendice.