Movie Recommendation System

Movie recommendation systems aim to predict user preferences and provide personalized film suggestions, enhancing user experience and engagement. Current research focuses on improving recommendation accuracy and relevance through advanced machine learning techniques like matrix factorization (including SVD and NMF), clustering algorithms (like K-means), and deep learning models incorporating visual and textual data from movie posters, trailers, and descriptions. Furthermore, research actively addresses challenges such as mitigating harm from biased or inappropriate content, achieving group consensus in recommendations, and ensuring fairness in algorithms. These advancements have significant implications for both the scientific understanding of recommendation systems and their practical application in entertainment and e-commerce.