A Hybrid Approach towards Improving Performance of Recommender System Using Matrix Factorization Techniques
In today's digital world of information overload, the recommendation system plays an important role in the decision making of an individual as well as the collective societal level at large scale. The recommendation system is useful for leveraging the power of available data to create a better user experience and incremental revenue for the E-commerce companies. As per statistics from McKinsey about recommendation system gives 35 percent incremental revenue for Amazon, around 75 percent video consumption and 1billion $ saving for Netflix due to recommendations and around 60 percent of views on YouTube come from the leverage of powerful recommendations. This research paper shows, how a hybrid approach is useful to improve the prediction accuracy of the recommender system by using various matrix factorization techniques and regression on given sample data. Using all rating information from a large number of users and movies build a recommendation system that recommends/suggest a movie to concern user.