Building Better Recommendations with Graph Analytics in Snowflake

Headline: Deliver hyper-personalized recommendations that actually convert by moving beyond simple popularity.

About this Webinar: In this session, Corydon Baylor (Senior Technical Product Marketer at Neo4j) and Kevin Gomez (Solution Architect at Neo4j) discuss why traditional relational databases often fall short when building recommendation engines. They explore how graph-based approaches—like community detection and node similarity—provide more intuitive, explainable, and accurate results by looking at the “neighborhood” around your data points.

What You’ll Learn:
The “Relational to Graph” Shift: Why your business model is often already a graph and how to stop forcing it into tables.

Avoiding “The Banana Problem”: How node similarity (powered by the Jaccard coefficient) filters out “noisy” popular items to find truly personalized recommendations.

Snowflake Native Integration: A deep dive into the Neo4j Graph Analytics for Snowflake native app, showing how to run 65+ algorithms entirely within Snowflake using SQL.

The AI Connection: A look at how graph embeddings and LLMs (like Snowflake Cortex) can be used to further automate and enhance your data analysis.

Key Algorithms Mentioned:
Node Similarity: To find products with overlapping customer neighborhoods.

Community Detection: To group customers with similar tastes (The “Amazon/Netflix” approach).

Centrality: To identify bottlenecks or influencers within a network.

FastRP: For creating powerful graph embeddings for machine learning.

Resources:
Free E-book: Learn the math and intuition behind five of our most popular graph algorithms with “A Practical Introduction to Graph Algorithms.” https://bit.ly/4ryOw7m

Get Started for Free: Find Neo4j Graph Analytics in the Snowflake Marketplace with a 30-day free trial https://bit.ly/4aLAl7t