A Personalized Approach to OTT Content Discovery - StreamSync

Abstract: With the exponential growth of Over-the-Top (OTT) platforms, users are often overwhelmed by the sheer volume of content available, leading to challenges in discovering relevant movies and TV shows. This research paper presents a personalized OTT content discovery app developed using Next.js, Tailwind CSS, and the TMDB API. The application aims to enhance user experience by providing tailored recommendations based on individual preferences and viewing history. Leveraging the comprehensive data provided by the TMDB API, the app enables dynamic and responsive user interfaces for seamless navigation across content categories. The combination of Next.js for server-side rendering and Tailwind CSS for modern, responsive design ensures optimal performance and user experience. The proposed system effectively addresses the current limitations in content discovery by offering a highly customized, easy-to-navigate platform that improves user satisfaction. Initial testing shows promising results in recommendation accuracy and user engagement, with future plans to integrate advanced machine learning techniques for even more precise recommendations