Abstract
Recommendation System has been widely used in electronic commerce recently. To promote user’s visiting of websites, it recommends objects that users might be interested. Nowadays, large E-commerce sites often have millions of items and users, which increase the computation workload of Recommendation System rapidly. Apache Mahout, an open source machine learning library, which uses MapReduce framework to implement Collaborative Filtering Recommendation Systems, is desinged to make large-scale data process more efficient. MapReduce framework is a distributed computation programming model. It is first proposed by Google and applied to the development of many Google’s servies. Then Apache Hadoop developed its MapReduce Project which has more widespread applications. MapReduce is mainly used to do large-scale data processing by distributing data and computation to different nodes of Cluster. In this thesis we present the work of analyzing the processing of a Mahout Distributed Item Based Recommendation System and improving the most time comsuming part, which is the computation of similarity matrix. Two new algorithms of Distributed Collaborative-based Recommendation System are proposed and implemented using Stochastic SVD. Moreover, we conducted experiments to compare the performance and accuracy of those algorithms on two different clusters of Apache Hadoop servers. Experimental evaluations showed our algorithms and implementation can improve the performance of Mahout Distributed Item Based Recommendation System 2.5 times and its accuracy by Stochastic SVD features.