AI in Digital Entertainment: Exploring User-Centric Movie Prediction Systems

This paper explores the development and implementation of a movie recommendation system powered by Artificial Intelligence (AI), focusing on the use of collaborative filtering techniques to enhance user experience in digital streaming platforms. By leveraging machine learning algorithms such as K-Nearest Neighbours (KNN) and Singular Value Decomposition (SVD), the system analyses user preferences and interactions to generate personalized movie recommendations. The backend of the system is built using Flask, while the frontend is developed with HTML, CSS, and JavaScript, ensuring an intuitive and responsive user interface. Despite its effectiveness, the collaborative filtering approach faces challenges such as data sparsity and the cold start problem, which can hinder recommendation accuracy. This paper discusses the evaluation metrics employed, including Mean Squared Error (MSE) and Precision@K, to assess the performance of the system. It also highlights the potential for future improvements, such as integrating content-based filtering and hybrid models to enhance the adaptability and precision of the recommendations. By optimizing content discovery, the system aims to improve user engagement and satisfaction in the rapidly growing digital streaming market.