Movie Recommendation System Using Machine Learning

With the continuous expansion of digital streaming platforms, personalized movie recommendations have become essential to enhance user experience. This paper presents a machine-learning–based movie recommendation system utilizing content-based filtering, collaborative filtering, and hybrid approaches. The system is evaluated using the MovieLens dataset, and performance is measured using RMSE and MAE. The results indicate that hybrid models outperform individual approaches due to their ability to integrate user–item interactions with content features.