Monika Mishra, Mingoo Kang, Jongwook Woo, Journal of Internet Computing and Services, Vol. 21, No. 6, pp. 33-39, Dec. 2020
10.7472/jksii.2020.21.6.33, Full Text:  HTML
Keywords: Big data, Deep Learning, Spark, Analytics Zoo, Amazon EMR, Machine Learning, Recommendation

Abstract

The paper is to build recommendation systems leveraging Deep Learning and Big Data platform, Spark to predict item ratings of the Amazon e-commerce site. Recommendation system in e-commerce has become extremely popular in recent years and it is very important for both customers and sellers in daily life. It means providing the users with products and services they are interested in. Therecommendation systems need users’ previous shopping activities and digital footprints to make best recommendation purpose for next item shopping. We developed the recommendation models in Amazon AWS Cloud services to predict the users’ ratings for the items with the massive data set of Amazon customer reviews. We also present Big Data architecture to afford the large scale data set for storing and computation. And, we adopted deep learning for machine learning community as it is known that it has higher accuracy for the massive data set. In the end, a comparative conclusion in terms of the accuracy as well as the performance is illustrated with the Deep Learning architecture with Spark ML and the traditional Big Data architecture, Spark ML alone.

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