Recommender System for Movies using Collaborative Filtering

Abstract

Recommender systems are programs which attempt to predict items that a user may be interest in. Recommender systems acts personalized decision guides, aiding users in decisions on matter related to personal taste. Recommender system depends on information provided by different users to gather its knowledge. Collaborative Filtering (CF) become an important data mining technique to make personalized recommendations for books, web pages or movies, etc. One popular algorithm is the memory-based collaborative filtering, which predicts a user’s preference based on his or her similarity to other users (instances) in the database. Movie recommendation system demonstrates the advantages of multidimensional visualization of the recommender system’s results. In this paper, we implement a recommender system for movies using memory–based collaborative filtering method. Memory-based for collaborative filtering predicts average (weighted) rating between similar users or items.