Building Enterprise Recommendation Systems with Neo4j: From Data to Personalised Insights

Description

Samko Yun, Senior Solutions Engineer at Neo4j, brings extensive consulting experience across finance, retail, education, and other industries to explore how graph databases can power sophisticated recommendation engines. Through practical case studies and real-world examples, Samko will demonstrate how to leverage Neo4j's native graph capabilities to capture complex relationships between users, products, and content that traditional databases struggle to represent.

Abstract

In this session, we'll explore how Neo4j's native graph database capabilities can revolutionize recommendation systems by capturing complex relationships that traditional databases struggle to represent. Through practical case studies from finance, retail, and education industries, we'll demonstrate how to build sophisticated recommendation engines that go beyond simple collaborative filtering to deliver truly personalized insights.

Key Takeaways

• Understand how graph databases capture complex relationships for recommendation systems
• Learn to leverage Neo4j's native graph capabilities for personalized insights
• Explore practical case studies from finance, retail, and education industries
• Discover techniques for building sophisticated recommendation engines
• Gain insights into moving beyond simple collaborative filtering