Building a Product Recommendation Engine With Neo4j — No ML Library Required
When many developers think about recommendation engines, they think of machine learning: collaborative filtering models, matrix factorization, embedding vectors, and…
In the previous article, we built a custom Neo4j procedure that embeds DuckDB directly into the Neo4j JVM and makes it callable from Cypher. We showed how it works and ran a few examples to prove the concept. Now it's time to put it to work on something real.
In this article, we'll use that procedure on the NYC Taxi and Limousine Commission (TLC) trip record data for January 2024. The dataset covers nearly three million yellow cab trips and more than fifty thousand green cab trips across 265 taxi zones. Our goal is to aggregate those trips into a traversable graph in Neo4j — zones as nodes, routes as relationships — and then query it in ways that neither engine could handle as naturally on its own.
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