PERSONALIZED RECOMMENDATION ON MOVIESMETADATA USING HYBRID APPROACH

Recommendation systems are a form of data filtering that plays a crucial role in helping users discover relevant content,based on their interests and needs. They have become an integral feature of numerous platforms, particularly in digital streaming services, where users often struggle with decision fatigue due to vast content. However, challenges such as the coldstart problem, data sparsity, and evolving user preferences require advanced approaches that balance accuracy and scalability.This study presents a hybrid movie recommendation system that integrates content-based filtering and collaborative filtering techniques to generate highly relevant suggestions. The content-based approach leverages metadata from the Movie_Metadata and TMDB datasets, incorporating movie popularity and other relevant features. Collaborative filtering analyzes user interactions and preferences from the Movie_Metadata ratings dataset. By combining these techniques, the system effectively mitigates individual limitations, resulting in a robust and efficient recommendation engine. Experimental results demonstrate that the hybrid model outperforms standalone methods in user satisfaction. This scalable solution addresses the growing demands of streaming platforms and highlights the broader potential of hybrid recommendation systems across various domains.