Recommendation system to simplify opinion formation

This paper aims at introducing a new way of recommending movies to users. It is an improvement on the existing approaches of Content Based Recommendation system and Collaborative Filtering. Creating similar feature vectors for both the users and movies, we update it with every passing recommendation made. We then find out the nearest user by calculating the difference in the feature using root mean square error technique. We finally draw out a conclusion and observe the cases where this outperforms other popular algorithms. We also look at its shortcomings and list the scope for future improvements that could be made.