Research On E-commerce Personalized Recommendation Based On Cloud Computing

The continuous development of the information age, people fully enjoy the conveniencebrought by information. With the rapid development of e-commerce sites, online shopping hasbecome a part of people’s lives. However, with the increasing of the variety and numbers ofgoods, the increasing of the amount of information in e-commerce will spend consumers a lotof time finding the right products, in terms of the sellers, they hope to provide differentservices to different people in order to enhance the user’s satisfaction, then e-commercepersonalized recommendations emerged. The recommendation based on collaborativefiltering is a kind of widely used recommendation technologies, by analyzing the users’historical consumer records, analysining of user’s behavior, to understand and grasp the users’preferences, targeted recommended products to the users. Because of its reliablerecommendation, many experts and scholars has study and research the collaborativefiltering algorithm the recommendation accuracy has gradually increased, however thetraditional recommendation algorithms in dealing with mass-consumer information highlightsthe inadequacies of its recommended speed of a user’s shopping experience.The proposal of cloud computing has changed people’s understanding of computers, ithas a strong computing power and storage capacity, Usually users only need to purchase thecloud computing service that you can user any terminal to access cloud services, it overcomesthe limitations of traditional stand-alone or server, facilitates user access to computingresources and management. Therefore, this article by taking advantage of cloud computing toimprove the traditional collaborative filtering algorithms in terms of speed and scalabilityissues.In this paper, the experimental use open source cloud computing platform Hadoopplatform as the algorithm parallelization. Therefore, this paper do thorough research on itscore technology distributed file system HDFS and MapReduce paradigm, combined thecollaborative filtering based on items and based on matrix decomposition ALS, parallelize thealgorithms. Finally, experimental results also proved that the algorithm parallelization hasstrong scalability and efficiency.