A hybrid big data movies recommendation model based k-nearest neighbors and matrix factorization

On the subject of broadcasting information, finding someone’s favorite book or movie in a sea data containing books and movies has become crucial issue. In an era when there are so many genres types books, customer may find it difficult to choose which discover first place. Thus, personalized recommendation systems play important role because value that is attributed nowadays, considering from user not be able have specific target. this context, our proposed work, design implement prototype system while taking into consideration real requirement for search books. The research by using k-nearest neighbors approach collaborative filtering algorithm adopted extract criteria good use case on recommender systems. At last, results as what was expected they showed effect.