Hybrid Movie Recommendation System using React, Flask and Machine Learning

Movie recommendation systems play an important role in helping users discover relevant content on digital entertainment platforms. Traditional recommendation approaches often face challenges such as low personalization and limited recommendation accuracy. To address these issues, this project proposes a Hybrid Movie Recommendation System that combines Content-Based Filtering and Neural Collaborative Filtering techniques. The system uses TF-IDF Vectorization and Cosine Similarity to analyze movie features and generate recommendations based on movie similarity. Neural Collaborative Filtering is used to learn user preferences from rating data and improve personalization. The application is developed using React.js for the frontend, Flask for the backend, and Firebase for user authentication and database management. The proposed system generates personalized movie recommendations efficiently and provides a simple, interactive, and user-friendly experience for users.