Graph-Based Neo4j Recommendation Engine (Java SPRINGBOOT Microservices + Neo4j DB)

A production-style recommendation service using Neo4j, Java, and microservices architecture that supports the following:

• Collaborative filtering
• Item-based recommendations
• Hybrid recommendation models

This system demonstrates how graph databases can power real-time, relationship-driven recommendations, similar to platforms like Netflix, Amazon, or Spotify.

Problem Statement

Modern applications need to answer questions like:

• What should this user watch next?
• Which products are similar?
• What content is relevant based on user behavior?

Traditional databases struggle with these because of complex joins and relationship traversal.

This is where Neo4j (Graph Database) becomes powerful.

Why Neo4j for Recommendation Systems?

Recommendation systems are fundamentally about relationships:

• Users → Movies
• Users → Products
• Items → Similar Items

Neo4j allows:

• Fast traversal of relationships
• Real-time recommendations
• Flexible graph modeling
• Efficient similarity queries

Instead of complex SQL joins, recommendations become simple graph traversals.

System Architecture

Client Request
     ↓
Spring Boot API
     ↓
Recommendation Service
     ↓
Neo4j Graph Database
     ↓
Recommended Results

• REST APIs built using Spring Boot
• Graph queries executed using Cypher
• Results returned in real-time

Recommendation Techniques Implemented

1️⃣ Collaborative Filtering

Recommends items based on similar users.

Example:

Users who liked Inception also liked Interstellar

Graph idea:

(User)-[:LIKED]->(Movie)<-[:LIKED]-(OtherUser)

2️⃣ Item-Based Filtering

Recommends items based on similar items.

Example:

If you liked The Matrix, you may like John Wick

Graph idea:

(Movie)-[:SIMILAR_TO]->(Movie)

3️⃣ Hybrid Recommendation System

Combines:

• User similarity
• Item similarity
• Behavioral patterns

This improves accuracy and diversity of recommendations.

API Endpoints

The service exposes REST APIs for different recommendation strategies.

GET /v1/recommendations/collaborative/{userId}
GET /v1/recommendations/movie/{movieId}
GET /v1/recommendations/hybrid/{userId}

Performance & Optimization

While building this system, focus were on:

• Efficient graph traversal queries
• Limiting traversal depth
• Indexing frequently accessed nodes
• Reducing query complexity
• Designing optimized relationships

Graph databases shine when queries are designed correctly.

Real-World Use Cases

This system can be adapted for:

• Movie / content recommendations
• E-commerce product suggestions
• Social network connections
• Learning platforms (course recommendations)
• Knowledge graph-based suggestions

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Github repo : recommendation-service

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