NLP-Powered Recommendation Engine with Flask Interface
Julian Menezes, G. Uma Maheswari, S. Shiraj, Lakshmi Narasimha Rao M, Deepak N · 2024
This project revolves around the development of a sophisticated Movie Recommendation Engine, propelled by the transformative impact of digitalization on the entertainment industry. The era of personalized, on-demand viewing experiences necessitates a solution to the paradox of choice faced by users amidst the vast content landscape. The Movie Recommendation Engine addresses this challenge, driven by the vision of creating a seamless and enjoyable viewing experience through personalized recommendations. The project goes beyond conventional recommendation algorithms, embracing a holistic approach to content discovery that introduces users to hidden gems and lesser-known titles within their preferred genres. Leveraging Natural Language Processing (NLP) technologies, the engine deciphers user preferences through sentiment analysis, ensuring nuanced insights from textual data. The deployment phase involves a Streamlit interface for an interactive user experience, showcasing personalized recommendations and fostering user engagement. The project's scope encompasses diverse cinematic tastes, promising a comprehensive solution for a global audience.