Machine Learning Techniques for Movie Recommendation: A Matrix Factorization Perspective

Ashish Kumar Nayyar

doi:10.65301/iitm.2026.17.1.1

Articles Volume 17 Issue 1 Jan - June 2026

DOI 10.65301/iitm.2026.17.1.1

Machine Learning Techniques for Movie Recommendation: A Matrix Factorization Perspective

Authors

AS

Ashish Kumar Nayyar

Assistant Professor, Department of Computer Science, Institute of Information Technology & Management, New Delhi

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Published 2026-07-22

Pages 01-12

Abstract

Recommender Systems have become a cornerstone of modern digital platforms; they are providing personalized content and suggestions there by significantly enhancing overall user experience. This paper explores the
real-world applications of recommender systems with a particular focus on the movie domain. The paper discusses various areas where such recommender systems can efficiently be deployed and used, highlighting their relevance in entertainment, e-commerce, and content delivery platforms. Movie recommendation systems, in particular, leverage machine learning techniques to analyze users’ past behavior, preferences, and viewing patterns to generate tailored suggestions. These systems improve user satisfaction and play an important role in driving user engagement
and platform retention. Moreover, the integration of contextual information—such as time, location, and user mood—further refines the recommendation process, making it more dynamic and responsive. This
paper present a comprehensive overview of different approaches of machine learning being used in building movie recommendation systems, including collaborative filtering, content-based filtering, and hybrid models. The associated challenges with data sparsity, scalability, and cold-start
problems are di, and propose potential solutions to address them. This paper aims to contribute to the growing body of knowledge in personalized recommendation by offering insights into both the technical foundations and practical implications of movie recommendation systems in today’s data-driven environment.

Keywords

Matrix Factorization Recommendation Systems Content-Based Filtering and Collaborative Filtering

References

Nilashi, M., Ibrahim, O. & Bagherifard, K. (2018). A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Syst. Appl., 92, 507–520. https://doi.org/10.1016/j.eswa.2017.09.058.
Thorat, P.B., Goudar, R.M. & Barve, S. (2015). Survey on collaborative filtering, content-based filtering and hybrid recommendation system, Int. J. Comput. Appl., 110(4).
Geetha, G., Safa, M., Fancy, C. & Saranya, D. (2018). A hybrid approach using collaborative filtering and content based filtering for recommender system. Journal of Physics: Conference Series, 1211.
Liao, M., Sundar, S.S. & Walther, J.B. (2022). User trust in recommendation systems: A comparison of content-based, collaborative and demographic filtering. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, (pp. 1–14).
Uta, M. et al., (2024). Knowledge-based recommender systems: overview and research directions, Front. Big Data, 7, 1304439.
Bodduluri, K.C., Palma, F., Kurti, A., Jusufi, I. & Löwenadler, H. (2024). Exploring the landscape of hybrid recommendation systems in e-commerce: A systematic literature review, IEEE Access, 12, 28273–28296.
Dhar, S. (2024). A Survey on domain of application of recommender system. In How Mach. Learn. is Innov. Today's World: A Concise Tech. Guid., (pp. 375–382).
Bobadilla, J., Dueñas-Lerin, J., Ortega, F. & Gutierrez, A. (2024). Comprehensive evaluation of matrix factorization models for collaborative filtering recommender systems, arXiv Prepr. arXiv:2410.17644.

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Ashish Kumar Nayyar . (2026). Machine Learning Techniques for Movie Recommendation: A Matrix Factorization Perspective. IITM Journal of Management and IT, 17(1), 01-12.

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IITM Journal of Management and IT

Published by Institute of Information Technology & Management

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[1] Nilashi, M., Ibrahim, O. & Bagherifard, K. (2018). A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Syst. Appl., 92, 507–520. https://doi.org/10.1016/j.eswa.2017.09.058.

[2] Thorat, P.B., Goudar, R.M. & Barve, S. (2015). Survey on collaborative filtering, content-based filtering and hybrid recommendation system, Int. J. Comput. Appl., 110(4).

[3] Geetha, G., Safa, M., Fancy, C. & Saranya, D. (2018). A hybrid approach using collaborative filtering and content based filtering for recommender system. Journal of Physics: Conference Series, 1211.

[4] Liao, M., Sundar, S.S. & Walther, J.B. (2022). User trust in recommendation systems: A comparison of content-based, collaborative and demographic filtering. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, (pp. 1–14).

[5] Uta, M. et al., (2024). Knowledge-based recommender systems: overview and research directions, Front. Big Data, 7, 1304439.

[6] Bodduluri, K.C., Palma, F., Kurti, A., Jusufi, I. & Löwenadler, H. (2024). Exploring the landscape of hybrid recommendation systems in e-commerce: A systematic literature review, IEEE Access, 12, 28273–28296.

[7] Dhar, S. (2024). A Survey on domain of application of recommender system. In How Mach. Learn. is Innov. Today's World: A Concise Tech. Guid., (pp. 375–382).

[8] Bobadilla, J., Dueñas-Lerin, J., Ortega, F. & Gutierrez, A. (2024). Comprehensive evaluation of matrix factorization models for collaborative filtering recommender systems, arXiv Prepr. arXiv:2410.17644.