Cinematic Curator: A Machine Learning Approach to Personalized Movie Recommendations

This work suggests a sophisticated movie recommendation system that offers individualized recommendations based on user preferences by combining content-based filtering, collaborative filtering, and deep learning approaches. The system use natural language processing (NLP) to examine user-generated content, movie summaries, and reviews in order to get a sophisticated comprehension of thematic aspects and narrative styles. The model includes SHAP for explainability to improve transparency and give consumers insight into the reasoning behind recommendations. The user-friendly interface, which is accessible via web and mobile applications, guarantees a smooth experience. The system is able to adjust to changing user preferences and market trends through ongoing upgrades that are founded on fresh data. The system's efficacy is validated by user research and A/B testing, which show precise and customized movie recommendations that satisfy a range of tastes.