Movies recommendation system using collaborative filtering and k-means

The purpose of this research is to develop a movie recommender system using collaborative filtering technique and K-means. Collaborative filtering is the most successful algorithm in the recommender system's field. A recommender system is an intelligent system that can help a user to come across interesting items. This paper considers the users m (m is the number of users), points in n dimensional space (n is the number of items) and we present an approach based on user clustering to produce a recommendation for the active user by a new approach. We used k-means clustering algorithm to categorize users based on their interests. We evaluate the traditional collaborative filtering and our approach to compare them. Our results show the proposed algorithm is more accurate than the traditional existing one, besides it is less time consuming than the previous existing methods.