A machine learning approach to movie recommendation system - Saw, Zi Jin (2025)
With the rapid growth of the digital entertainment industry and the increasing popularity of streaming platforms like Netflix and YouTube, personalized content delivery has become a critical focus. Intelligent recommendation systems are essential for enhancing user engagement, reducing decision fatigue, and promoting content discovery. This project presents a machine learning-based movie recommendation system aimed at providing accurate and personalized movie suggestions while addressing common industry challenges such as biased recommendations, data sparsity, and the cold start problem. Multiple algorithms—including K-Means with KNN, Singular Value Decomposition (SVD), and Matrix Factorization using Keras—were evaluated using Root Mean Square Error (RMSE) to identify the most effective model. A hybrid approach, integrating content-based and collaborative filtering techniques, was adopted to optimize recommendation accuracy and fairness. The final system is implemented as a web application with features such as secure login, dynamic movie interaction, and personalized profile management. This work demonstrates the potential of intelligent systems to improve user satisfaction in digital media platforms.