Movie Recommender System using Single Value Decomposition and K-means Clustering

Abstract

The recommender system is technique and tool to filter the massive overloaded information for suggesting most useful information to the user in a personalized manner. In the period of “Big Data”, the researcher experiences many problems to process big data accurately and efficiently. In this work, we introduce an efficient model using Single Value Decomposition (SVD) as a method for dimensions reduction and K-means clustering as classification method. Our proposed method and its corresponding results have been evaluated and compare results with other existing methods using metrics like standard deviation (SD), mean absolute error (MAE) root mean square error (RMSE), t-value, dunn index, average similarity and computational time using two publicly available datasets using Flixter dataset and MovieLens dataset. The result demonstrates that our proposed method is able to outperform other existing methods.