DECORS: A Simple and Efficient Demographic Collaborative Recommender System for Movie Recommendation

With the explosion of internet users on the web, finding the right choice for the user is a tedious task. Collaborative filtering one of the most commonly used Recommender system recommends the movies based on user preferences. However, it requires expensive computations to find the similarity as the number of users and movies increases. Hence, DECORS a Movie Recommender is proposed which provide a solution for the aforementioned problem. DECORS is based on collaborative filtering which initially partitions the users based on demographic attributes, then using kmeans clustering algorithm clusters the partitioned users based on user rating matrix. It reduces the expensive computations to identify similar users in order to predict movies when compared with traditional collaborative filtering approach. The proposed system also sorts the movies in the increasing order of user’s preferences. The proposed framework is assessed by using the performance measurement MAE(Mean Absolute Error). The results proved that proposed system is more efficient when compared against traditional methods.