Implementing Graph Databases to Improve Recommendation Systems in E-commerce

Authors

  • 1Vijay Mallik Reddy, 2Lakshmi Nivas Nalla 1Member of Technical Staff, University of North Carolina at Charlotte, Email: vijaymr1012@gmail.com 2Data Engineer Lead, Florida International University, 11200 SW 8th St, Miami, FL 33199, Email:nallanivas@gmail.c Author

Keywords:

Graph databases, Recommendation systems, E-commerce, Personalization, Data modeling.

Abstract

Graph databases have emerged as a powerful tool for enhancing recommendation systems in e-commerce platforms. By modeling complex relationships between users, products, and their attributes as a graph structure, graph databases enable more accurate and personalized recommendations. This paper explores the implementation of graph databases to improve recommendation systems in e-commerce, highlighting their advantages over traditional relational databases. Through a review of existing literature and case studies, we examine the effectiveness of graph databases in capturing nuanced user preferences, identifying latent patterns, and delivering context-aware recommendations. By leveraging the inherent graph structure of e-commerce data, businesses can enhance customer engagement, increase conversion rates, and drive revenue growth in an increasingly competitive market.

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Published

2022-12-31

How to Cite

Implementing Graph Databases to Improve Recommendation Systems in E-commerce. (2022). Journal Of Environmental Sciences And Technology, 2(2), 99-110. https://jest.com.pk/index.php/jest/article/view/31