Аналіз методів персоналізації контенту в IT-проєктах електронної комерції на базі поведінкових патернів користувачів

Abstract

The report discusses the analysis of methods for personalizing recommendations based on user behavioral data and their application in e–commerce ІТ projects. The analysis focuses on analyzing existing recommendation methods, their advantages, and limІТations, as well as exploring ways to improve their effectiveness in e-commerce systems. The research highlights the role of machine learning and data analysis techniques in enhancing recommendation systems, particularly in addressing challenges such as the "cold start" problem, dynamic user preferences, and data sparsІТy. The results of the study aim to contribute to the development of more adaptive and accurate recommendation systems in e-commerce.