ONLINE MOVIE STREAMING WEBSITE

Abstract

The rapid growth of online video consumption has increased the demand for streaming platforms capable of personalizing user experiences and processing large-scale data efficiently. This project develops an online movie-streaming website that integrates an intelligent recommendation system based on user watch history and interaction behavior. The methodology includes database design for movies, series, seasons, and episodes; backend development using Node.js/Express with MongoDB; implementation of a hybrid recommendation model (collaborative filtering + content-based); and a ReactJS frontend optimized for user experience. Experimental results indicate that the system performs reliably, provides fast search responses, and generates accurate personalized recommendations. In conclusion, the proposed model effectively enhances user engagement and demonstrates strong potential for scaling to larger real-world applications.