Technology has evolved a lot from basic to advanced such as Machine learning, deep learning, Internet of things, Data Mining and many more. Recommender systems provide users with personalized suggestions for products or services also this system only rely on collaborative filtering. Movies are the source of Entertainment but finding the desired content is the problem. Aim of this paper is to improve the accuracy and performance of the regular filtering technique and also to recommend movies based on the content of the movie which users have watched earlier. Collaborative filtering recommends movies to user A based on the interest of similar user B. Netflix is internally using a cinematch algorithm for the collaborative filtering we are improving the accuracy and the performance of regular technique. Content based filtering will help Netflix boost their turnover by providing similar movies which users have watched earlier on any of the OTT(Over The Top) platforms. We have used a surprise library along with the xgboost regressor which makes our model improve from regular technique. We have also designed the frontend for the content based recommendation system system for Netflix.

Keywords : Content Based , Collaborative Filtering, Recommender System, Surprise-Library, User-Based Recommender, Item-Based Recommender.