Movie recommendation system using machine learning

Abstract

A recommendation system is a system that provides online users with recommendations for particular resources, such as books, movies, and music, based on a collection of data from the dataset. These recommendation systems are beneficial for companies that collect information from a large number of customers and aim to provide the best recommendations for the users.Withine-commerce, recommendation systems help in personalizing the user experience by providing items that the user is most likely to be interested in based on similar items that the user has been interested in or items that similar users have been interested in. Using datasets from kaggle to help us analyse the accuracy of our model.Many factors, including the genre of the movie, the actors, and even the director, should be considered while developing a movie recommendation system. The algorithms can recommend movies based on one or a combination of twoor more criteria by using collaborativeand content-based filtering techniques in machine learning.The cold-start problem, or the first lack of item reviews, is a new user issue that affects the most of these techniques. Here provided fresh user demographic data instead of rating history to produce suggestions in order to avoid the cold-start problem in this project.