Netflix Movies Recommendation System

Abstract

Netflix is a leading global entertainment company with over 247 million paying customers who can access TV series, films, and games in over 190+ countries. Many languages and genres are offered for the content. As much as they like, whenever and wherever they wish, participants are free to change their plans and play, pause, and resume viewing. With the exponential growth of content on streaming platforms like Netflix, users often face the challenge of finding relevant and enjoyable movies.The "Cold-Start" issue is a recommendation system issue wherein new users or items have little data, making it challenging to provide appropriate recommendations. In response, this paper proposes a data-driven movie recommendation system tailored for Netflix. Leveraging machine learning techniques, user preferences, and historical data, the model aims to enhance the user experience by providing personalized movierecommendations. This study explores algorithms such as collaborative filtering, content-based filtering, and hybrid models to achieve accurate and effective movie recommendations. While content-based filtering uses item features to suggest shows or movies a user has previously enjoyed, collaborative filtering examines user behavior and preferences to produce suggestions. Even with insufficient data, the system can deliver more accurate recommendations for new users or things by combining various approaches. By making recommendations based on both item features and user behavior, this hybrid approach helps to reduce the cold start issue.