Graph-Powered Recommendations: Developing and Deploying Recommendations

Recommendation engines have become a crucial component of modern applications of all types. This core need has triggered a shift from relational and big-data approaches to graph-based technologies that are purpose-built to handle the rigorous demands of real-time recommendations.

This week, in the final blog of our five-part series on graph-powered recommendations, we take a look at developing and deploying recommendation applications. We also give an overview of the advantages of deploying your recommendation engine on Neo4j.

Developing & Deploying Recommendation Applications

The Neo4j Intelligent Recommendations Framework is the most productive, powerful and customizable environment for developing and deploying recommendation applications. The framework combines:

• The dependability and performance of the Neo4j graph database
• Powerful development and testing tools specifically designed for making intelligent recommendations
• Customizable deployment alternatives for application and organizational requirements

Development Advantages

The framework slashes development and testing times for recommendation applications by 50% by enabling developers to:

• Create recommendation engines using a zero-code approach
• Turn blocks of code on/off with a click to simplify testing and control phased rollouts
• Compile discrete engine phases at runtime to increase performance without losing maintainability
• Create fully functional GraphQL APIs automatically as they build recommendation engines and pipelines
• Improve maintainability by separating Cypher queries for engine phases rather than requiring gigantic Cypher statements
• Order recommendation results subjectively to promote selected products, bundles or pricing strategies

Deployment Advantages

Organizations have many options for deploying their Neo4j recommendation applications. Information managers maximize dependability, availability and performance by:

• Taking full advantage of Neo4j’s High Availability, load balancing and server clustering architecture
• Configuring independent clusters for customer data and the Recommendations Framework to provide extreme scalability, balancing and fault tolerance
• Deploying recommendation apps with or without internet access, on local networks, or even on single, disconnected machines
• Scheduling recommendation engines to run at specific, predetermined times

Why Use Neo4j for Recommendations?

When you select Neo4j as the foundation for your recommendation systems, you transform the way your customers and employees interact with your online systems.

Real-Time Recommendations

When making recommendations, time is of the essence. The speed and flexibility of Neo4j’s graph technology enables you to offer real-time recommendations for the first time. By reducing recommendation calculations from minutes to milliseconds, eBay cites that “Neo4j allows us to add functionality that was previously not possible.”

Works with All Data Sources

As an open framework, Neo4j links to networked data sources and supports graph-based relationships that transcend all those sources. As a result, it maintains top performance for even your most complex recommendation algorithms and business environments.

Deployed Easily to All Applications

Neo4j recommendation engines can be independently deployed and scaled to meet the performance and load-balancing needs of existing applications and portals. Neo4j stores framework data logically separate from customer data to maintain the integrity, privacy and security requirements of your internal and customer information.

Widest Choice of Scoring Methods

The Neo4j framework combines graph-based queries and algorithms for scoring recommendations and enables you to create weighted scores based on multiple techniques in real time – resulting in more accurate, context-aware recommendations.

Flexible, Highly Configurable Engines

Neo4j gives you a fast and easy way to build modular, configurable pipelines that tune recommendations to the specific requirements of every use case and context. Their modularity gives them an unprecedented level of configurability and makes them trivial to modify and maintain.

Higher Quality Recommendations

Effective recommendations have obvious value to your users and are meaningful in their current context. Using Neo4j’s flexible graph approach, your recommendations are valuable, explainable and relevant to your users.

Fast to Develop, Maintain and Expand

The application development framework in Neo4j enables you to focus on just the business logic of recommendations rather than writing infrastructure code. The result is applications with up to ten times less code – sometimes even code-free – decreasing your time to market with a recommendation engine that is easy to develop, support and maintain.

Achieve Business Objectives

The bottom line for all recommendation engines is how effectively they achieve your business objectives. The power, flexibility and high performance of Neo4j ensure your recommendations are on time and on target, so they meet the revenue and business goals of your organization.

Conclusion

As we have shown, the Neo4j Intelligent Recommendations Framework is the most productive, powerful and customizable environment for developing and deploying recommendation applications.

When you select Neo4j as the foundation for your recommendation systems, you transform the way your customers and employees interact with your online systems.