Abstract
Recommendation systems play a key role in the digital environment, providing personalized recommendations in online stores, streaming services, social networks, and educational platforms. This paper presents a comprehensive review of recommendation system models, including content and collaborative filtering, hybrid approaches, and state-of-the-art algorithms based on deep learning, reinforcement learning, and graph neural networks. The advantages and disadvantages of different methods, their accuracy, performance, scalability and adaptability to new data are analyzed. The main challenges such as the cold-start problem, data sparsity, bias of algorithms, the need for explainability of recommendations and privacy assurance are reviewed. Special attention is paid to the prospects of implementing recommendation systems in educational platforms. The importance of using hybrid and intelligent systems to effectively analyze user data and build recommendations tailored to individual needs is emphasized. The conclusion is drawn about further development of recommendation systems, which will be associated with the integration of the latest artificial intelligence technologies, optimization of computational resources and expansion of their application area in various digital ecosystems. The work can be useful for researchers, developers and practitioners working in the field of artificial intelligence and educational technologies.