Simulation of genre based movie recommendation system using Hadoop MapReduce technique

Recommendation systems have gained tremendous popularity over the past few years. As we all know recommendation systems have provided a boon in the field of online shopping and other browsing portals. Recommendation systems use machine learning techniques to predict what the user may like based on his history of interaction with a system full of items. At present there are many approaches known to implement a recommendation system. These approaches have several algorithms with various efficiencies. The efficiency and the accuracy of the recommendation system solely depend on the algorithm used. Hence, we've designed, implemented, and successfully deployed a system that uses an ensemble of item-, and hybrid-based recommendation systems. In this paper we are going to explain how the results from two algorithms which are run on Hadoop are combined to get more accurate movie recommendations. [1]

I. Introduction

On the online transaction and any other internet related things, where many choice is mange, there are many problem of information overload of data, there are many number of data that are difficult to manage, which created many problem to users. These problems are solved by the recommender systems, the system will generated the information that used to provide users with their personalized content and services. [18]