The earliest commercial leaders using graph databases as a core technology platform were internet giants like Google, Facebook and LinkedIn. While those pioneers had to build their own in-house solutions from scratch, Neo4j now offers a commercially-ready graph application platform to any business wanting to make the most of real-time recommendations.
Here are some snapshots of how Neo4j is helping modern organizations build real-time recommendation solutions that catapult them to the forefront of their markets.
Walmart has sales of almost 500 billion dollars annually and serves more than 260 million customers weekly. The leading retailer uses Neo4j to serve up real-time recommendations on its websites in 11 countries.
“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,” claims software developer Marcos Wada. “With enterprise features for scalability and availability, Neo4j is the right choice to meet our demands.”
eBay uses the delivery coordination platform Shutl to enable the fast fulfillment of online and mobile orders quick and convenient. Switching from relational to Neo4j allowed eBay merchants to quickly match orders with same-day delivery couriers, enables their online merchants to compete toe-to-toe with brick-and-mortar stores. eBay also uses a graph-powered suggestion engine to perform sophisticated, real-time package routing.
Senior developer Volker Pacher says that Neo4j is “literally thousands of times faster than our prior MySQL solution, with queries that require 10 to 100 times less code. Today, our graph database provides eBay with functionality that was previously impossible. We achieved constant query performance by using Neo4j to create a graph that is its own index. That’s awesome development flexibility.”
The adidas Group wanted to aggressively step up its game by offering a personalized shopping experience to its online customers worldwide. But as with many large retailers, adidas had a variety of data silos storing information about its products, markets, social media, master data, digital assets, brand content and operational systems.
Using Neo4j, adidas was able to bridge the silos with a common metadata service and a recommendation engine to offer relevant, real-time suggestions to shoppers across online, social and mobile channels.
After users spend a few moments specifying what they like, Precise Target gives them personalized recommendations on apparel from over 300 merchants. It uses Neo4j to make billions of calculations to find products loved by other users with similar tastes to provide real-time recommendations.
Andy Rosenbaum, Vice President of Engineering and Technology at Precise Target, said: “The graph database allows us to explore new connections between people, giving a very personalized experience of products we think they’ll love.”
Each month, Israel-based Wobi provides millions of users with best-value offers for insurance and financial products. The structure and type of data files that Wobi stores data in a tree-like structure in which each customer sits atop a branch of information. After object-based approaches we dismissed by developers, Wobi selected Neo4j as its best option for accessing and updating individual data on the fly while delivering lightning-fast recommendations.
Neo4j is currently handling half a million customers with an average of eight pensions, policies and products each—for a total of 4 million nodes and a staggering 30 million relationships—and has the capacity to expand much further. Discussing Wobi’s selection of Neo4j, CTO Shai Bentin commented, “Describing a relationship is important. To model this in an object-oriented way would be lengthy…. And I feel safe with Neo4j.”
Movie Recommendation Site
A film recommendation website is revolutionizing how movie studios promote new releases by letting fans discover upcoming movies they’ll like before they debut on the big screen. It then provides the studios with the fans’ preferences and behaviors, enabling the studios to effectively target marketing campaigns.
The website considered using a relational approach for its recommendation system, but after seeing the amount of data required, they chose Neo4j. Their CTO said, “We wanted to quickly connect audiences to the right movies, and Neo4j just fits our philosophical standpoint. We are very happy that we discovered Neo4j. We increased the speed of generating recommendations and users to movies, which is a core part of our business model.”
International Fortune 500 Company
After being plagued by significant slowdowns with its relational, real-time pricing recommendation engine, an international Fortune 500 company moved to a Neo4j graph-database solution. The move to Neo4j has improved performance so substantially that they have seen a 300% growth in the volume of generated recommendations, while slashing system hardware requirements.