Machine Learning Techniques for Movie Recommendation: A Matrix Factorization Perspective

Recommender Systems have become a cornerstone of modern digital platforms; they are providing personalized contentand suggestions thereby 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.