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Journal of Information

June 2021, Volume 6, 1, pp 1-14

Big Data Frameworks for Sites and Products Recommendation

Ogbuju, E.

,

Ejiofor, V.

,

Okonkwo, O.

,

Onyesolu, M.

Ogbuju, E. 1

Ejiofor, V. 2 Okonkwo, O. 2 Onyesolu, M. 2 

  1. Department of Computer Science, Federal University Lokoja; Department of Computer Science, Nnamdi Azikiwe University, Awka, Nigeria. 1

  2. Department of Computer Science, Nnamdi Azikiwe University, Awka, Nigeria. 2

Pages: 1-14

DOI: 10.18488/journal.104.2021.61.1.14

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Article History:

Received: 22 March, 2021
Revised: 26 April, 2021
Accepted: 14 May, 2021
Published: 04 June, 2021


Abstract:

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.
Contribution/ Originality
This study documents the use of the cross-industry standard process for data mining methodology to design big data frameworks that implement the application of sentiment analysis of review texts as preprocessed input into the collaborative filtering algorithm for both sites and product recommendation.

Keywords:

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

Reference:

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Funding:

This study received no specific financial support.

Competing Interests:

The authors declare that they have no competing interests.

Acknowledgement:

All authors contributed equally to the conception and design of the study.

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