Recommendation System based on Content Rating

Abstract

The advent of streaming services has made it easier to watch films and television series. The movie industry has been growing rapidly over time. The enormous amount of content available nowadays makes it challenging for users to choose what to watch. Movie recommendation systems have been developed to assist customers in selecting films based on their individual preferences. This facilitates and amuses the choosing process. These systems employ a number of strategies to offer customers personalized suggestions. One of the most popular techniques is collaborative filtering, which suggests films that users may also like based on their tastes and watching history. Another method is content-based filtering, which utilizes the traits of movies—like genre, stars, and directors—to suggest others with comparable qualities. To provide suggestions that are more accurate, hybrid methods that integrate the two methodologies have also been created. It emphasizes how crucial personalisation is to recommendation systems since it raises user engagement and pleasure. The performance of movie recommendation systems may be increased by adding user input as well as cutting-edge methods like deep learning and natural language processing.