CinePulse: OTT Platform Recommendation System Project

This paper presents CinePulse, an intelligent machine learning–based OTT recommendation system developed to assist viewers in efficiently discovering movies and web series that closely align with their tastes and preferences. The system analyzes rich content metadata, including genres, cast, crew, descriptions, release year, and audience ratings, and applies a cosine similarity–based content filtering approach to identify meaningful relationships between titles in a multidimensional feature space. A robust hybrid architecture is implemented where the React.js frontend delivers an interactive, responsive user interface, while FastAPI acts as the communication layer between the client application and a Python-driven recommendation engine. The model is trained on a structured dataset compiled from multiple OTT sources, enabling the generation of highly relevant suggestions without requiring user login credentials or historical viewing patterns. CinePulse supports features such as real-time search, metadata-based filtering, dynamic content suggestions, and seamless UI interactions, ensuring an engaging discovery experience for users. The results demonstrate that similarity-based recommendation techniques can effectively generate precise and scalable OTT recommendations using metadata alone, making CinePulse a lightweight yet powerful solution for modern content discovery platforms.