Сучасні методи побудови рекомендаційних систем

Abstract

This work examines modern recommendation systems by reviewing key methods – collaborative filtering, content-based approaches, hybrid models, knowledge-based systems, and deep learning. It highlights that while collaborative filtering efficiently handles large datasets, it suffers from cold start issues, and content-based methods offer strong personalization yet may miss novel suggestions; hybrid models mitigate these limitations despite higher computational demands. Ultimately, the study emphasizes the need for dynamic updates and ethical practices to build effective, personalized recommendation systems.