A Dual-Method Movie Recommendation System: Content and Collaborative
N Gowthami, S. Balaji, S Gowshikan, S Mathesh · 2025
In the dynamic landscape of personalized media consumption, efficient movie recommendation systems are vital for improving user engagement. This paper introduces a dual-method system that combines content-based and collaborative filtering to deliver personalized recommendations. Using metadata such as cast, genre, and plot, along with user ratings, the system employs TF-IDF vectorization and cosine similarity for content analysis and Singular Value Decomposition (SVD) for collaborative filtering. Evaluated on a comprehensive movie ratings dataset, the hybrid model shows superior performance over traditional methods in terms of precision, recall, and F1 score, offering a robust solution to movie recommendation challenges.