Film Flare: Movie Recommendation System Using Machine Learning

Abstract

In today's digital entertainment world, the abundance of content on various streaming platforms often leads to user indecision and dissatisfaction. As a solution, recommendation systems play a vital role in filtering content to match user interests. This research presents a robust hybrid recommendation model that merges collaborative and content-based filtering to enhance recommendation quality. Drawing on a rich dataset of 5,000 movies from Kaggle, the model incorporates user ratings and comprehensive movie metadata, including genres, cast, and plot keywords. By integrating both historical user behavior and movie content features, this hybrid approach alleviates the limitations of standalone methods and delivers a personalized viewing experience. The implementation, carried out using Python in Google Colab, validates the model's practicality and scalability through detailed evaluation and real-time demonstrations.