REVIEW OF RECOMMENDER SYSTEMS: MODELS AND PROSPECTS FOR USE IN EDUCATIONAL PLATFORMS

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.

1. Gomez-Uribe C.A. The Netflix recommender system: Algorithms, business value, and innovation / C.A. Gomez-Uribe, N. Hunt // ACM Transactions on Management Information Systems. – 2022. – № 6(4). – Р. 1-19. https://doi.org/10.1145/2843948.

2. Pazzani M.J. Content-based recommendation systems / M.J. Pazzani, D. Billsus // In The adaptive web. – 2007. – Р. 325-341. https://doi.org/10.1007/978-3-540-72079-9_10.

3. Item-based collaborative filtering recommendation algorithms / В. Sarwar et al // Proceedings of the 10th International Conference on World Wide Web. – 2001. – Р. 285-295. https://doi.org/10.1145/371920.372071.

4. An algorithmic framework for performing collaborative filtering / J.L. Herlocker et al // Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. – 1999. – Р. 230-237. https://doi.org/10.1145/312624.312682.

5. Deshpande, M. Item-based top-N recommendation algorithms / M. Deshpande, G. Karypis // ACM Transactions on Information Systems. – 2004. – № 22(1). – Р. 143-177. https://doi.org/10.1145/963770.963776.

6. Koren Y. Matrix factorization techniques for recommender systems / Y. Koren, R. Bell, C. Volinsky // Computer. – 2009. – № 42(8). – Р. 30-37. https://doi.org/10.1109/MC.2009.263.

7. Burke R. Hybrid web recommender systems / Burke R. // In The adaptive web. – 2007. – Р. 377408. https://doi.org/10.1007/978-3-540-72079-9_12.

8. Session-based recommendations with recurrent neural networks / В. Hidasi et al // Proceedings of the International Conference on Learning Representations (ICLR). – 2016. https://doi.org/10.48550/arXiv.1511.06939.

9. Attention is all you need / A. Vaswani et al // Advances in Neural Information Processing Systems. – 2017. https://doi.org/10.48550/arXiv.1706.03762.

10. Deep reinforcement learning for page-wise recommendations / Х. Zhao et al // Proceedings of the 12th ACM Conference on Recommender Systems. – 2018. Р. 95-103. https://doi.org/10.1145/3240323.3240357.

11. Top-K off-policy correction for a REINFORCE recommender system / М. Chen et al // Proceedings of the 12th ACM International Conference on Web Search and Data Mining. – 2019. – Р. 456-464. https://doi.org/10.1145/3289600.3290999.

12. Hamilton W.L. Representation learning on graphs: Methods and applications / W.L. Hamilton, R. Ying, J. Leskovec // IEEE Data Engineering Bulletin. – 2017. – № 40(3). – Р. 52-74. https://doi.org/10.48550/arXiv.1709.05584.

13. Session-based recommendation with graph neural networks / S. Wu et al // Proceedings of the AAAI Conference on Artificial Intelligence. – 2019. – № 33(1). – Р. 346-353. https://doi.org/10.1609/aaai.v33i01.3301346.