Big Data Frameworks for Sites and Products Recommendation




The improvement of the IT infrastructure in an e-commerce platform is essential for both customer satisfaction and increased revenue. While different techniques had been applied towards achieving this, there is still need to engage customer feedbacks in providing an all-inclusive solution to the recommendation systems available in the e-commerce domain. The motivation is on making a more exact recommendation with the traditional collaborative system by mining the feedbacks and uncovering their sentiments using big data analytic systems. This paper describes the design of a big data framework that may be used for shopping sites recommendations and another that may be used for product(s) recommendations to prospecting customers. The use of the cross industry standard process for data mining is applied in proposing the new system. Although the techniques of Hadoop/MongoDB tools are described within the proposed designs, it concentrates mainly on the architecture and algorithm of the system in a holistic approach to enable the platform providers, e-commerce merchants and practitioners find a guided implementation of it using any tool of choice.


Sentiment analysis, Collaborative filtering, Big data, CRISP-DM, Recommender, E-commerce

Abstract Video



How to Cite

E, O., V, E., O, O., & M, O. (2021). Big Data Frameworks for Sites and Products Recommendation. Journal of Information, 6(1), 1–14.