Collaborative Filtering Method for Data Rating Prediction

Abstract

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 user-based approach of Neighborhood-based collaborative filtering method. User-based collaborative filtering gives personalized recommendations by finding similar users.  And then the accuracy of the algorithm is calculated using Mean Absolute Error (MAE). The result of MAE is better in user-based CF method.