Published Paper Details:

System presents a hybrid movie recommendation system designed to merge collaborative filtering, content-based filtering, and cosine similarity, offering users personalized suggestions rooted in their preferences and viewing history. Built as a web application with an HTML/CSS frontend, the system dynamically retrieves movie data via APIs to circumvent static dataset limitations. User engagement is heightened through visual comparisons of watched and recommended content. Sentiment analysis of reviews, implemented using Support Vector Machines (SVM), further refines recommendation accuracy. By integrating collaborative and content-based methods, the system addresses challenges like data sparsity and the cold start problem. Future plans include transitioning the platform to Flutter for improved interactivity and mobile compatibility. System underscores the efficacy of hybrid models in enhancing recommendation diversity and user satisfaction.