Платформа персонализированных рекомендаций медиаконтента на основе коллаборативной фильтрации

Авторы и правообладатели:

• Мелехов Н. А. , Томский государственный университет систем управления и радиоэлектроники (Томск, Россия)
• Якименко В. С. , Томский государственный университет систем управления и радиоэлектроники (Томск, Россия)
• Сенченко П. В. , Томский государственный университет систем управления и радиоэлектроники (Томск, Россия)

• 1. Taramov A.A., Shilov N.G. [Recommender Systems for Driver Information Support: State-of-the-Art Review]. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, 2018, vol. 21, no. 2, pp. 68–74. DOI: 10.21293/1818-0442-2018-21-2-68-74 (in Russ.).
• 2. Kataev M.Yu., Loseva N.V., Bulyševa L.A. [Structure of an Information Recommender System for Decision Support in the Provision of Services by a Public Institution]. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, 2018, vol. 21, no. 2, pp. 83–87. DOI: 10.21293/1818-0442-2018-21-2-83-87 (in Russ.).
• 3. Roy D., Dutta M. A Systematic Review and Research Perspective on Recommender Systems. Journal of Big Data, 2022, vol. 9, art. 59. DOI: 10.1186/s40537-022-00592-5.
• 4. Afoudi Y., Lazaar M., Al Achhab M. Hybrid Recommendation System Combined Content-Based Filtering and Collaborative Prediction Using Artificial Neural Network. Simulation Modelling Practice and Theory, 2021, vol. 113, art. 102375. DOI: 10.1016/j.simpat.2021.102375.
• 5. Zangerle E., Bauer C. Evaluating Recommender Systems: Survey and Framework. ACM Computing Surveys, 2022, vol. 55, no. 8, pp. 1–38. DOI: 10.1145/3556536.
• 6. Panteli A., Boutsinas B. Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns. Algorithms, 2023, vol. 16, no. 4, art. 182. DOI: 10.3390/a16040182.
• 7. Python documentation. Available at: https://docs.python.org/ (accessed: 16 April 2026).
• 8. PostgreSQL: Documentation. Available at: https://www.postgresql.org/docs/ (accessed: 16 April 2026).
• 9. Flutter documentation. Available at: https://docs.flutter.dev/ (accessed: 16 April 2026).
• 10. Serverpod documentation. Available at: https://docs.serverpod.dev/ (accessed: 16 April 2026).
• 11. Lercher A., Glock J., Macho C., Pinzger M. Microservice API Evolution in Practice: A Study on Strategies and Challenges. Journal of Systems and Software, 2024, vol. 215, art. 112110. DOI: 10.1016/j.jss.2024.112110.
• 12. Material Design 3. Available at: https://m3.material.io/ (accessed: 17 April 2026).
• 13. Churikov E.A., Zudilova T.V., Ananchenko I.V., Osipov N.A., Ivanov S.E., Osetrova I.S. [Comparison of Modern Mobile Application Development Tools]. Modern High Technologies, 2022, no. 12, pp. 82–87. DOI: 10.17513/snt.39441 (in Russ.).
• 14. Souha A., Benaddi L., Ouaddi C., Jakimi A. Comparative Analysis of Mobile Application Frameworks: A Developer’s Guide for Choosing the Right Tool. Procedia Computer Science, 2024, vol. 236, pp. 597–604.
• 15. Oliveira W., Moraes B., Castor F., Fernandes J.P. Analyzing the Resource Usage Overhead of Mobile App Development Frameworks. EASE ’23: Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, University of Oulu, Finland, ACM, 2023, pp. 152–161. DOI: 10.1145/3593434.3593487.