Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{193611,
        author = {Prathishkumar M and Aparna C and Muthazhagan S and Harishya M and Febin Amose F D},
        title = {An AI-Driven Intelligent Movie Discovery Platform and Real-Time Adaptive Recommendation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {700-704},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193611},
        abstract = {The rapid expansion of digital streaming services and Over-The-Top (OTT) platforms has significantly increased the availability of movie content across diverse genres, languages, and narrative styles. It also creates challenges in effectively discovering movies that align with individual tastes and contextual preferences. Most existing recommendation systems rely on static filters such as genre, ratings, or viewing history, often resulting in repetitive suggestions and limited personalization. Furthermore, conventional systems lack deep natural language understanding and adaptive learning capabilities necessary to interpret nuanced user intent. This paper presents CineBook, an AI-powered intelligent movie discovery platform designed to enhance personalized content exploration through advanced Natural Language Processing (NLP) and adaptive recommendation mechanisms. The proposed system interprets free-form user queries —such as mood-based or story-driven prompts—by analyzing intent, themes, and narrative preferences in real time. Additionally, CineBook supports voice- enabled interaction through integrated hardware components, providing an accessible and conversational discovery experience across web, mobile, smart TV, and AI-assisted devices. By combining intent analysis, context-aware feature extraction, and real- time orchestration of recommendations, the platform improves personalization accuracy, discovery efficiency, scalability, and overall user engagement.},
        keywords = {CineBook, Intelligent Movie Discovery, Artificial Intelligence, Adaptive Learning, Personalized Recommendation System.},
        month = {March},
        }