Solutions: Real-Time Recommendation Engines

 

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.

 

Related Material

  • 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.

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  • 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

Challenges

Why Neo4j?

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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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Other Solutions

  • Real-time analysis of data relationships is essential to uncovering fraud rings and other sophisticated scams before fraudsters and criminals cause lasting damage.

    Queries: Anti Money Laundering (AML), Ecommerce Fraud, First-Party Bank Fraud, Insurance Fraud, Link Analysis

  • Tap into the power of graph-based search tools for better digital asset management using the most flexible and scalable solution on the market.

    Queries: Asset Management, Cataloging, Content Management, Inventory, Work Flow Processes

  • Graph databases are inherently more suitable than RDBMS for making sense of complex interdependencies central to managing networks and IT infrastructure.

    Queries: Asset Management, Cybersecurity, Impact Analysis, Quality-of-Service Mapping, Root Cause Analysis