Maximize the value of customer-product interactions
Model customers, products, and their interactions as a connected graph. Analyzing those relationships gives you insight into habits and preferences that aggregate metrics miss entirely.
Recommendation Engine
Recommendations start with relationships.
Customers and products are connected in ways that tabular data can't capture. Memgraph models those relationships as a graph and runs recommendations as simple queries on live data.
The problem
SQL was never built for this.
Schema changes break as habits change
Customer behavior evolves constantly. Relational schemas that work today require painful migrations tomorrow. Graph models adapt without restructuring the database.
Multi-hop recommendations require complex JOINs
"Customers who bought this also bought that" is a two-hop graph traversal. In SQL, it's a series of nested JOINs that degrade under load and become unmaintainable as the data grows.
Recommendations on stale data miss the moment
Batch-computed recommendations reflect yesterday's behavior. Graph databases run on live data so recommendations adapt to what customers are doing right now.
How Memgraph Helps
From customer behavior to accurate recommendations in real time.
Start simple, scale to production
Build your recommendation pipeline incrementally: from simple collaborative filtering to full graph neural network-powered recommendations. Each step runs on the same graph, no migration required.
Complex insights as simple Cypher queries
Graph neural networks, community detection, and collaborative filtering algorithms are all available as built-in MAGE procedures. Call them directly from Cypher - no complex JOINs, no custom code.
WhitepaperGraph Technology in Recommendation Engines
How graph databases and purpose-built algorithms provide accurate, real-time recommendations that keep up with the growth and complexity of customer data.Download whitepaper
Capabilities
Why Memgraph.
Built for real-time, write-heavy recommendation workloads.
Performance
High Availability
Flexibility
Why Are SQL Databases Outdated for the Real-Time Recommendation Engines
If a recommendation engine built on relational databases is falling apart due to the bottlenecks made by complex JOINs and never-ending schema changes, there is only one permanent and game-changing solution: graph databases.
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