Prediction of Movie Rating Using Item-Based Collaborative Filtering Method

Abstract

Recommender systems employ prediction algorithms to provide users with items that match their interests. One of the most famous recommender systems is a collaborative filtering (CF) method. The system is designed to evaluate the recommender system using Neighborhood-based collaborative filtering (CF) methods.  The evaluation using MovieLens offline datasets is implemented using the timestamp values of user ratings of movies to improve the accuracy. This system generates the prediction accuracies of item-based approach of Neighborhood-based collaborative filtering method. Item-Based collaborative filtering recommends similar items. And then the accuracy of the algorithm is calculated using Mean Absolute Error (MAE). The results of MAE are better in item-based CF method.