The evolution of movie recommendation systems began in the late 1990s alongside the emergence of ecommerce platforms and online streaming services. Early systems primarily utilized collaborative filtering, which based recommendations on user behavior and shared preferences. Traditionally, movie suggestions came from friends, family, critics, or television programs, relying heavily on word-of-mouth, printed reviews, and broadcasted opinions. However, this conventional method lacked personalization and scalability, often resulting in recommendations that did not align with individual preferences. The shift toward machine learning-based recommendation systems was driven by the need for more accurate, scalable, and personalized solutions that could adapt to diverse user tastes. These modern systems analyze vast volumes of data to detect hidden patterns and provide tailored viewing suggestions. The proposed recommendation system incorporates collaborative filtering, content-based filtering, and hybrid models to enhance accuracy and user satisfaction. Collaborative filtering identifies similar user behavior to suggest relevant content, while content-based filtering leverages movie attributes such as genre, cast, and director to match user interests. Hybrid approaches combine both strategies to address limitations and improve overall performance. The system continuously collects and analyzes user interactions, including viewing history, ratings, and search activity, while applying advanced machine learning algorithms such as matrix factorization and deep learning to uncover latent features and evolving patterns. Additionally, it can integrate supplementary data sources like social media engagement and demographic information to further refine recommendations. This comprehensive approach delivers a seamless and personalized user experience, significantly boosting engagement and satisfaction on platforms like Netflix and Amazon Prime.
Rao et al. (Thu,) studied this question.