With the rapid growth of online streaming platforms, providing personalized movie recommendations has become essential for enhancing user experience and engagement. This study focuses on developing a movie recommendation system using a collaborative filtering approach. The collaborative method relies on analyzing user preferences, viewing histories, and rating patterns to predict and suggest movies that align with a user’s interests. By leveraging user-item interaction matrices, the system identifies similarities among users or movies using techniques such as user-based and item-based collaborative filtering. The study also explores the integration of matrix factorization and similarity algorithms to improve accuracy and scalability. Experimental results demonstrate that the collaborative approach effectively captures user preferences and delivers relevant recommendations compared to traditional content-based methods. The proposed model contributes to improving recommendation accuracy, user satisfaction, and personalization in modern entertainment platforms.
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