Russian Federation
from 01.01.2023 until now Voronezh, Russian Federation
This article examines the principles underlying the design of personalized recommender systems based on two fundamental approaches: content-based filtering and collaborative filtering. In an era of information overload and rising user expectations, personalization has become a critical component of digital products whether streaming services, e-commerce platforms, or news aggregators. Effective recommendation algorithms not only enhance user engagement and conversion rates but also foster a more meaningful and diverse user experience. The paper provides a detailed analysis of the mathematical foundations, strengths, and limitations of each approach, their evolution under the influence of modern machine learning techniques, and practical implementation considerations including contextual awareness, quality evaluation, and ethical challenges. Special emphasis is placed on hybrid architectures that combine the advantages of both methods to achieve maximum relevance and robustness in recommendations.
recommender systems, content-based filtering, collaborative filtering, personalization, machine learning, user experience
1. Sumin, V. I. Ispol'zovanie situacionnogo modelirovaniya v razrabotke sistem prinyatiya resheniy dlya slozhnyh organizacionnyh sistem / V. I. Sumin, A. S. Kravchenko, A. V. Tolkachev // Modelirovanie sistem i processov. – 2024. – T. 17, № 3. – S. 71-79. – DOIhttps://doi.org/10.12737/2219-0767-2024-69-77. – EDN FBBHJO.
2. Sumin, V. I. Razrabotka setevoy modeli celevyh ustanovok slozhnyh organizacionnyh sistem special'nogo naznacheniya / V. I. Sumin, A. S. Kravchenko, S. V. Rodin // Modelirovanie sistem i processov. – 2024. – T. 17, № 3. – S. 79-87. – DOIhttps://doi.org/10.12737/2219-0767-2024-77-85. – EDN QIRWOK.
3. Eremin O. Yu., Morkulev D. V. Metody realizacii gibridnyh rekomendatel'nyh sistem // E-Scio. - 2023. - № 3(78). - S. 52-62. EDN: https://elibrary.ru/OKQQKH
4. Popkov S. S., Gavryushin A. V. Sravnitel'nyy analiz algoritmov postroeniya i ocenka kachestva rekomendatel'nyh sistem // Informacionnye tehnologii i matematicheskie metody v ekonomike i upravlenii (ITiMM-2024): sb. st. XIII Mezhdunar. nauch.-prakt. konf. im. A.I. Kitova: v 3 t. Moskva, 14-15 marta 2024 g. - M.: Ros. ekon. un-t im. G.V. Plehanova, 2024. - S. 64-71. EDN: https://elibrary.ru/HTEIXP
5. Timofeev A.A. Mehanizmy raboty rekomendatel'nyh sistem i ih primenenie // Materialy Vserossiyskoy nauchnoy konferencii "Social'nyy inzhener - 2022". M., 2022. S. 7-13. EDN: https://elibrary.ru/LJSLSK
