Proceedings of TUSUR University / Archive / Issue of Journal № 1, t. 29, 2026 / Platform for Personalized Media Content Recommenda-tions Based on Collaborative Filtering

Platform for Personalized Media Content Recommenda-tions Based on Collaborative Filtering

DOI: 10.21293/1818-0442-2026-29-1-95-101

Download article in PDF format

JATS xml

Abstract: Relevance. The growing volume of digital media content exacerbates the problem of information overload and complicates the selection of relevant items for the user. The purpose of the study is to design a personalized media content recommendation platform focused on movies, TV series, books, anime, and video games. Methods. The study employs collaborative filtering based on cosine similarity of users, categorical preference analysis, as well as methods for designing client-server architecture, collecting and normalizing media data. Novelty. A unified recommendation environment for different types of media content is proposed, where recommendations are generated considering both the similarity of user ratings and explicit genre and categorical preferences in cases of data scarcity. Results. The platform architecture, recommendation generation mechanism, media data preparation framework, and computational experiment design for evaluating the quality of recommendations and server-side characteristics were developed. Practical significance. The proposed solution can be used as the basis for a cross-platform service for personalized media content selection.

Keywords: recommendation system, media content, personalization, collaborative filtering, user preferences, mobile application, flutter, serverpod, client-server architecture

Funding: The work was carried out within the state assignment of the Ministry of Education and Science of Russia; project FEWM-2026-0011.

For citation:
Melehov N. A., Yakimenko V. S., Senchenko P. V. Platform for Personalized Media Content Recommenda-tions Based on Collaborative Filtering. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, 2026, vol. 29, no. 1, pp. 95–101. DOI: 10.21293/1818-0442-2026-29-1-95-101

Authors and copyright holders:

Melehov N. A. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)
Yakimenko V. S. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)
Senchenko P. V. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)

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.