Adaptive AI-Driven Hybrid Movie Recommendation System with Real-Time Personalization

Abstract

This paper proposes an Adaptive AI-Driven Hybrid Movie Recommendation System designed to provide highly personalized and dynamic movie suggestions by leveraging real-time user interactions and advanced machine learning techniques. The system integrates multiple recommendation approaches, including contentbased filtering and collaborative filtering, within a hybrid framework to overcome common limitations such as cold-start issues and data sparsity. User data—such as ratings, browsing history, and interaction patterns—is continuously collected and processed through a structured pipeline. Key features like genre, cast, director, and metadata are extracted and transformed into numerical vectors, enabling similarity computation using techniques such as cosine similarity and matrix factorization. The proposed system dynamically updates recommendations in real time, adapting to evolving user preferences and improving accuracy and relevance over time. The architecture comprises a well-structured dataset, feature extraction module, machine learning models, and an interactive user interface to ensure a seamless user experience. Experimental insights demonstrate enhanced recommendation accuracy, diversity, and scalability compared to traditional static systems. This research highlights the practical implementation of Artificial Intelligence in building adaptive recommender systems and its potential applicability across various domains such as e-commerce, music streaming, and online learning platforms.