AI-Driven Personalized Movie Recommendations: A Content and Sentiment-Aware Model for Streaming and Digital Entrepreneurship - Shelake, Vijay; Fernandes, Scott; Shrungare, Sarthak (Pandawan, 2025)
In an era marked by the digital consumption of media, the landscape of movie recommendation is undergoing a profound transformation. Traditional recommendation methods, which rely on collaborative filtering and user reviews, are being supplanted by more sophisticated content-based approaches. The evolution of Artificial Intelligence (AI) has given rise to a new generation of recommendation systems, characterized by their ability to process and analyze vast amounts of content metadata to provide tailored suggestions. This research presents an AI-driven personalized movie recommendation model for streaming and digital entrepreneurship, leveraging data analytics and Natural Language Processing (NLP) techniques to enhance user experience. The model integrates sentiment analysis and cosine similarity to recommend similar movies, offering personalized recommendations across multiple streaming platforms, thus improving user satisfaction, engagement, and content discovery. By utilizing AI-driven algorithms, this model contributes to digital entrepreneurship by enhancing content personalization and improving user retention in the competitive streaming industry.