SMART MOVIE RECOMMENDER: LEVERAGING COLLABORATIVE FILTERING FOR ENHANCED USER EXPERIENCE

This project presents a movie recommendation system using collaborative filtering, which predicts user preferences based on the behavior of similar users. Unlike content-based approaches that rely on movie attributes, this method uses past user interactions to generate personalized and diverse suggestions. The system, implemented in Python, processes user IDs, movie ratings, item IDs, and timestamps to compute correlations and similarities between users. Key advantages of the approach include independence from item metadata, scalability across various content types, and the ability to suggest unexpected yet relevant items. Inspired by real-world systems like Netflix and Amazon, this model aims to improve recommendation accuracy and user satisfaction through data-driven personalization.