MovieRecommendationSystem

ABSTRACT OF THE PROJECT:- A Recommendation engine filters the data using different algorithms and recommends the most relevant items to users. It is a type of information filtering system which attempts to predict the preferences of a user, and make suggests based on these preferences, especially in streaming services. For streaming services like Netflix, recommendation systems are essential for helping users find new movies to enjoy. Objective of the recommendation system is to achieve customer loyalty by providing relevant content and maximising the time spent by a user on your website or channel. This also helps in increasing customer engagement. Three main approaches are used for recommender systems. One is Demographic Filtering i.e They offer generalized recommendations to every user, based on movie popularity with similar demographic features. Second is Content-based filtering, where users interests are profiled using information collected, and recommend items based on that profile. The other is collaborative filtering, where we try to group similar users together and use information about the group to make recommendations to the user. In this project, we propose a machine learning approach to produce a Content-based filtering system which predicts movie recommendations for a user based on large database of continuously updated movies. Need of Movie Recommendation System – Helps the item provider (ex. Netflix/Amazon) to deliver their items to the right user – Websites like Netflix can improve user-engagement – It increases revenues for business through increased consumption.