Efficient Recommender System for Over-the-Top Media Service

Abstract

Recommender System’s main idea is to decide the most suitable products for the customers. It enhances the relation between the users and the products. There are various applications of Recommender Systems. One such application that is most needed as well as helpful for users is the Movie Recommender System. A Movie Recommender System helps the users to find the movies those are more appropriate for them and which they may like the recommender system considers the user preferences to recommend movies to the users. There are various factors that can be considered to recommend a movie to the users. They are actors, genre and language of the movies. It will also consider the history of the movies watched by a particular user to recommend movies to them. The dataset that will be used for this project is Netflix prize dataset and hotstar dataset. Two models will be developed in this project namely Collaborative Filtering Algorithm and Pearson’s R Correlation algorithm. The outcome of this recommender system will be a customized list of top-rated movies from Netflix and Hotstar respectively. The future scope of this system is to recommend top rated movies from various OTT platforms which will help the user to identify his/her favorites in single application.