Movies Recommender Systems Using K-Means Based Methods

Abstract

Movies are not unknown entertainment to people in the 21th century. Then a Movie Recommender System is developed to take the huge treasure of movies closer to people over the world. The Movie Recommender System is usable for customers to enjoy what they want in a large variety of movies on any websites quickly. The performance of the recommendation function can be modified by many reasons, such as, customer’s behavior, statistic of rating and so on. The focus of the study is to exploit the data of movies and ratings to distinguish the groups of movies and preferably recommend genres of movies to customers.There was a public and popular data source for evaluating Movie Recommender System is MovieLens dataset, which is considered and examined for assessing the proposed method. The K-Means works on the data insert and determines the movies into particular groups. Each handling a specific character is different from the others. MapReduce is considered as the method to slip the number of operations during the K-means’s process.