Power More Accurate Recommendations in Real Time

Real-time recommendation engines are key to the success of any online business. To make relevant recommendations in real time requires the ability to correlate product, customer, inventory, supplier, logistics and even social sentiment data. Moreover, a real-time recommendation engine requires the ability to instantly capture any new interests shown in the customer’s’ current visit – something that batch processing can’t accomplish. Matching historical and session data is trivial for a graph database like Neo4j.

The key technology in enabling real-time recommendations is the graph database, a technology that is fast leaving traditional relational databases behind. Graph databases easily outperform relational and other NoSQL data stores for connecting masses of buyer and product data (and connected data in general) to gain insight into customer needs and product trends.

Fast Track

  • Shaping Up a Fitness Program Recommendation Engine & More

    See how Benjamin Nussbaum builds a personalized recommendation engine for BeachBody fitness programs, nutritional supplements and more.

    Read more
  • Powering Recommendations with a Graph Database

    Learn how companies like eBay and Walmart are using graph databases to power their real-time recommendation engines.

    Download the white paper
  • Webinar: Product Recommendations with MongoDB and Neo4j

    Watch how MongoDB can be used to provide search and browsing functionality for a product catalog while using Neo4j to provide personalized product recommendations.

    Watch the webinar

Business Outcomes

Personalize user recommendations

Whether you’re leveraging declared social connections or connecting the dots between seemingly unrelated facts to infer interests, graphs offer a world of fresh possibility when it comes to making better real-time recommendations for your users. Connect people to products, services, information or other people based on their user profile, preferences and past online activity such as product purchases.

Multi-criteria search

Enable users to search for products, services or people based on a host of fine-grained criteria and continually improve recommendations by accommodating new data sources and types – without an intensive re-write of your data model.

Challenges

Highly interconnected data

Whether the recommendation engine uses collaborative- or content-based filtering, it needs to traverse a continually growing, highly interconnected dataset.

Real-time query performance

The power of a recommender system lies in its ability to make a recommendation in real time employing users’ immediate history. However, traversing a complex and highly interconnected dataset to provide contextual insights is a challenge without the right technology.

Growing number of nodes

The accuracy and the scope of recommendations increase as you add more nodes or data points. The rapid growth in the size and number of data elements means the suggestion system needs to accommodate both current and future requirements.

Why Neo4j?

Native graph store

Unlike relational databases, Neo4j stores interconnected user and purchase data that is neither purely linear nor hierarchical. Neo4j’s native graph storage architecture makes it easier to decipher suggestion data by not forcing intermediate indexing at every turn.

Flexible schema

Neo4j’s versatile property graph model makes it easier for organizations to evolve real-time recommendation engines as data types and sources change.

Performance and scalability

Neo4j’s native graph processing engine supports high-performance graph queries on large user datasets to enable real-time decision making.

High availability

The built-in, high-availability features of Neo4j ensure your user data is always available to your mission-critical recommendation engine.

Walmart-logo

How Walmart Powers Real-Time Recommendations with Neo4j

"Neo4j helps us to understand our online shoppers' behavior and the relationship between our customers and products, providing a perfect tool for real-time product recommendations... Neo4j is the right choice to meet our demands. It suits our needs very well."
–Marcos Wada, Software Developer, Walmart

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White Paper: Sustainable Competitive Advantage

White Paper: Sustainable Competitive Advantage

Creating Business Value through Data Relationships

Where does sustainable competitive advantage come from? It’s not from data volume or velocity, but from the knowledge of relationships in your data.

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Ready to get started?

Your enterprise is driven by connections – now it's time for your database to do the same. Click below to download and dive into Neo4j for yourself – or download the white paper to learn how to leverage the power of graph technology for more relevant and personalized recommendations.

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