movie recommendation system

computer science engineering

The exponential growth of the internet and the increasing popularity of online video streaming platforms have resulted in a vast amount of video content available on the web. With this enormous amount of content, movie recommendation systems have become an essential component of these platforms to aid users in finding relevant and personalized content. In recent years, machine learning techniques have shown to be an effective tool for building recommendation systems, particularly collaborative filtering and content-based filtering techniques.In this research paper, we present a movie recommendation system using machine learning algorithms. The system is designed to provide personalized movie recommendations to users based on their viewing history, preferences, and ratings. The proposed system uses collaborative filtering and content-based filtering techniques to provide movie recommendations to users. The system is developed using Python programming language and scikit-learn machine learning library. The system is evaluated using the MovieLens dataset, which contains movie ratings by users. The performance of the system is measured using various evaluation metrics, including mean absolute error (MAE), root mean squared error (RMSE), and precision-recall curve. The results show that the proposed system provides accurate and personalized movie recommendations to users.Overall, the proposed movie recommendation system using machine learning algorithms demonstrates the effectiveness of machine learning techniques in building personalized recommendation systems. The system can be integrated into online video streaming platforms to improve user experience and satisfaction by providing personalized and relevant movie recommendations.

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