Algorithm for movie recommendation system using collaborative filtering

Abstract Recommender systems are information filtering system that predicts the rating for users and items, basically from big data to recommend their likes. Movie recommendation systems provide a mechanism to assist users in classifying users with similar interests. Most traditional recommender systems lack accuracy in the case where data used in the recommendation process is sparse. This study addresses the sparsity problem and aims to eliminate it through a singular value decomposition collaborative filtering approach applied to a web-based movie recommendation system. This paper proposed a movie recommendation system whose primary objective is to suggest a recommender list through singular value decomposition collaborative filtering and cosine similarity. We enhance the model with a factorization form, which greatly reduces the model's number of parameters with a controlled complexity. This paper proposed a movie recommendation system whose primary objective is to suggest a recommender list through singular value decomposition collaborative filtering and cosine similarity. The present work improves these approaches by taking the movies' content information into account during the item similarity calculations. The proposed approach recommends the top n recommendation list of movies to users on user's interest preferences that were not already rated. Graphically shows the percentage of already viewed movies by user and movies recommended to User.