Recommendation systems help users find and select items (e.g., books, movies, restaurants) from the huge number available on the web or in other electronic information sources. Given a large set of items and a description of the user’s needs, they present to the user a small set of the items that are well suited to the description. Similarly, a movie recommendation system provides a level of comfort and personalization that helps the user interact better with the system and watch movies that cater to his needs. Providing this level of comfort to the user was our primary motivation in opting for movie recommendation system as our BTECH FINAL YEAR PROJECT. The chief purpose of our system is to recommend movies to its users based on their viewing history and ratings that they provide. The system will also recommend various E-commerce companies to publicize their products to specific customers based on the genre of movies they like. Personalized recommendation engines help millions of people narrow the universe of potential films to fit their unique tastes. Collaborative filtering and content based filtering are the are prime approaches to provide recommendation to users. Both of them are best applicable in specific scenarios because of their respective ups and downs. In this paper we have proposed a mixed approach such that both the algorithms complement each other thereby improving performance and accuracy of the of our system.