A Machine Learning Approach to Movie Recommendation Systems

In the era of digital content explosion, users are often overwhelmed by the vast amount of available movies on streaming platforms. Movie recommendation systems play a crucial role in personalizing user experience by suggesting relevant content. This research paper presents a comprehensive study of machine learning approaches for movie recommendation systems, focusing on collaborative filtering, content-based filtering, and hybrid models enhanced by deep learning techniques. Traditional methods often suffer from challenges such as data sparsity and cold-start problems. To address these issues, we propose a hybrid machine learning model integrating neural collaborative filtering with content-based features. The system utilizes the Movie Lens dataset for evaluation and employs performance metrics such as Root Mean Square Error (RMSE), Precision, and Recall. Experimental results demonstrate that the proposed model outperforms traditional approaches in accuracy and recommendation diversity. Furthermore, the integration of deep learning enables the extraction of latent features and nonlinear user-item interactions, significantly improving recommendation quality. This study highlights the effectiveness of machine learning in building scalable and efficient movie recommendation systems and provides insights into future advancements in personalized recommendation technologies.