Implementing Graph Databases to Improve Recommendation Systems in E-commerce

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.