Memory-Based Personalized Movie Recommender System

Abstract

The World Wide Web information grows explosively in the Internet and people encounter problem to pick some correct things from the overwhelming set of choices. The recommender systems help them choose something they actually want or need. Therefore, the recommender systems get the vital role in the Internet. One of the most successful technologies for recommender systems is called collaborative filtering. In this movie recommender system use memory-based collaborative filtering and firstly, the user has to give rating what he likes in the movie he has seen. It depends on that rating to find out the neighbors of an active user by using Pearson correlation coefficient method. To predict the movies that the active user has not seen depends on neighbors of the active user and the highest rating will be shown as recommended movie list.