Analysis and Use of MapReduce for Recommender Systems

摘要

MapReduce is a programming model for developing scalable parallel applications for processing large data sets, an execution framework that supports the programming model and coordinates the execution of programs and an implementation of the programming model and the execution framework. The goal of the thesis is to analyse MapReduce and to use it on two examples of recommender systems. The goal is achieved by developing the computation with MapReduce successfully. At first the programming model and the execution framework are analysed and three implementations for MapReduce: Hadoop MapReduce, MongoDB and MapReduce-MPI Library are compared. It is discovered that Hadoop MapReduce is the most suitable implementation for developing the selected examples of recommender systems as it provides fault tolerance and data reproduction which ensure reliability. Then the selected examples of recommender systems are developed using Cloudera QuickStart VM which is a one node Hadoop cluster.