K-Means Clustering based Solution of Sparsity Problem in Rating based Movie Recommendation System

Movie Recommendation is more useful in our community life due to its strength in giving enhanced entertainment. Recommendation system can advise a collection of movies to users depend on their choice, or the popularities of the movies. while, a set of motion picture recommendation systems have been planned, mainly of these either cannot advise a movie to the presented users powerfully. In this paper we propose to solve the sparsity problem in movie recommendation system that has the ability to recommend movies to a new user as well as the others. It mines movie databases to collect all the important information, such as, popularity and attractiveness, required for recommendation. But in Recommendation system has many problems like sparsity, cold start, first Rater problem, Unusual user problem. K-mean clustering is the most successful method of Recommender System. K-means clustering also K-Means Clustering. The Algorithm K-means (MacQueen, 1967) is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori.