Magento 2 Graph Database Integration: Use Cases for Recommendations

Magento 2 and Graph Database Integration: Use Cases for Recommendations

Ever wondered how big eCommerce sites like Amazon or Netflix always seem to know exactly what you want? That’s the magic of recommendation engines—and guess what? You can bring that same magic to your Magento 2 store by integrating a graph database.

In this post, we’ll explore why graph databases are perfect for powering product recommendations, how to set them up with Magento 2, and some real-world use cases to inspire your next big upgrade.

Why Graph Databases for Recommendations?

Traditional relational databases (like MySQL) are great for structured data, but they struggle with complex relationships. Graph databases, on the other hand, excel at mapping connections—exactly what you need for personalized recommendations.

Here’s why:

  • Speed: Traversing relationships is lightning-fast, even with millions of products.
  • Flexibility: Easily model customer behavior, product affinities, and purchase patterns.
  • Scalability: Handle growing data without performance bottlenecks.

Popular Graph Databases for Magento 2

Some top contenders:

  • Neo4j: The most popular graph database, with a robust query language (Cypher).
  • ArangoDB: A multi-model database that supports graphs alongside documents and key-value.
  • Amazon Neptune: A fully managed graph database service from AWS.

Setting Up Neo4j with Magento 2

Let’s walk through a basic integration using Neo4j.

Step 1: Install Neo4j

First, install Neo4j on your server or use their cloud service:

wget -O - https://debian.neo4j.com/neotechnology.gpg.key | sudo apt-key add -
echo 'deb https://debian.neo4j.com stable latest' | sudo tee /etc/apt/sources.list.d/neo4j.list
sudo apt-get update
sudo apt-get install neo4j
sudo systemctl enable neo4j
sudo systemctl start neo4j

Step 2: Configure Neo4j

Access the Neo4j browser at http://localhost:7474 and set up credentials.

Step 3: Connect Magento 2 to Neo4j

Use a PHP client like Neo4j-PHP-OGM or a REST API wrapper. Here’s a simple example:

composer require graphaware/neo4j-php-client

Then, create a helper class in Magento:


<?php
namespace Vendor\Module\Model;

use GraphAware\Neo4j\Client\ClientBuilder;

class Neo4jConnector
{
    private $client;

    public function __construct()
    {
        $this->client = ClientBuilder::create()
            ->addConnection('default', 'http://neo4j:password@localhost:7474')
            ->build();
    }

    public function getClient()
    {
        return $this->client;
    }
}

Use Case 1: "Customers Who Bought This Also Bought"

This classic recommendation relies on purchase history. Here’s how to model it in Neo4j:

// Create product nodes
CREATE (p1:Product {id: 'prod1', name: 'Wireless Headphones'})
CREATE (p2:Product {id: 'prod2', name: 'Bluetooth Speaker'})
CREATE (p3:Product {id: 'prod3', name: 'Phone Case'})

// Create customer and relationships
CREATE (c1:Customer {id: 'cust1', email: 'user@example.com'})
CREATE (c1)-[:BOUGHT]->(p1)
CREATE (c1)-[:BOUGHT]->(p2)
CREATE (c1)-[:BOUGHT]->(p3)

Now, query for recommendations:

MATCH (p:Product {id: 'prod1'})<-[:BOUGHT]-(c:Customer)-[:BOUGHT]->(rec:Product)
WHERE p <> rec
RETURN rec.name, count(*) as strength
ORDER BY strength DESC
LIMIT 5

Use Case 2: Personalized Browsing Recommendations

Track viewed products and suggest similar items:

// Track a view
MATCH (c:Customer {id: 'cust1'}), (p:Product {id: 'prod1'})
MERGE (c)-[v:VIEWED]->(p)
ON CREATE SET v.timestamp = timestamp()
ON MATCH SET v.timestamp = timestamp()

// Get recommendations based on views
MATCH (c:Customer {id: 'cust1'})-[v:VIEWED]->(p:Product)
WITH c, p, v.timestamp as time
ORDER BY time DESC
LIMIT 5
MATCH (p)-[:SIMILAR_TO]->(rec:Product)
WHERE NOT (c)-[:BOUGHT]->(rec)
RETURN rec.name
LIMIT 3

Use Case 3: Bundling Recommendations

Suggest complementary products that are frequently bought together:

MATCH (p1:Product {id: 'prod1'})<-[:BOUGHT]-(c)-[:BOUGHT]->(p2)
WHERE p1 <> p2
WITH p1, p2, count(*) as frequency
ORDER BY frequency DESC
LIMIT 1
RETURN p2.name as recommended_bundle

Performance Considerations

While graph databases are powerful, keep these tips in mind:

  • Indexing: Always index frequently queried properties.
  • Batch Processing: For large datasets, process recommendations offline.
  • Caching: Cache recommendation results to reduce database load.

Taking It Further

Want to get really advanced? Try:

  • Incorporating machine learning to weight recommendations
  • Adding real-time recommendation updates
  • Segmenting recommendations by customer groups

Graph databases open up a world of possibilities for personalization in Magento 2. By understanding your customers’ relationships with products, you can create shopping experiences that feel almost psychic—and that’s a surefire way to boost conversions and customer loyalty.

Ready to implement graph-powered recommendations in your store? Check out our Magento extensions that can help streamline the integration!