An E-commerce recommendation systems based on analysis of consumer behavior models

Abstract

The article presents some models and strategies of consumer behavior in electronic commerce systems. An analysis of e-commerce recommender systems was also carried out, including context-aware recommender systems on the example of the analysis of regions of Ukraine and weather conditions, etc. The core of the novelty of the research is primarily related to hybrid behavioral models, personalization, contextual recommendations, especially integrating contextual information, such as location, time of day, or user intent, evaluation metrics, etc. The impact of machine learning and artificial intelligence on e-commerce recommender systems and e-commerce systems in general is also explored. The article also presents the microservice architecture of an electronic store with recommender system API. The architecture includes several key components, such as client-side APIs for managing orders, products and carts, services for processing customer requests and interacting with databases, and data processing technology using artificial intelligence.