Unlike other business data, recommendations must be inductive and contextual in order to be considered relevant by your end consumers.
With a graph database, you capture a customer’s browsing behavior and demographics and combine those with their buying history to instantly analyze their current choices and then immediately provide relevant recommendations – all before a potential customer clicks to a competitor’s website.
In this “Graph Databases in the Enterprise” series, we’ll explore the most impactful and profitable use cases of graph database technologies at the world’s leading organizations. Last week, we examined fraud detection.
This week, we’ll take a closer look at real-time recommendation engines.
The Key Challenges in Building a Real-Time Recommendation Engine:
With so much data to track and process in a short amount of time, creating a recommendation engine capable of relevant, real-time suggestions isn’t easy. Here are some of the biggest challenges involved:
- Process large amounts of data and relationships for context Collaborative and content-based filtering algorithms rely on rapid traversal of a continually growing and highly interconnected dataset.
- Offering relevant recommendations in real time The power of a suggestion system lies in its ability to make a recommendation in real time using immediate purchase or browsing history.
- Accommodate new data and relationships continually The rapid growth in the size and number of data elements means your suggestion system needs to accommodate both current and future requirements.
Why Use a Graph Database to Power Real-Time Recommendation Engines?
Real-time recommendation engines provide a key differentiating capability for enterprises in retail, logistics, recruitment, media, sentiment analysis, search and knowledge management.
The key technology in enabling real-time recommendations is the graph database. Graph databases also out-class other database technology for connecting masses of buyer and product data (or connected data in general).
Making effective real-time recommendations depends on a database that understands the relationships between entities, as well as the quality and strength of those connections.
Only a graph database efficiently tracks these relationships according to user purchase history, interactions and reviews to give you the most meaningful insight into customer needs and product trends.
Graph-powered recommendation engines can take two major approaches:
- Identifying resources of interest to individuals
- Identifying individuals likely to be interested in a given resource
Examples: Walmart and eBay
Retail industry leader Walmart has sales of more than $460 billion and employs 2.2 million associates worldwide, serving more than 245 million customers weekly through its 11,000 stores in 27 countries and e-commerce websites in 10 countries.
Their development team has decided to use a graph database to serve up real-time product recommendations by using information about what users prefer.
Walmart Software Developer Marcos Wada states that a graph database “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.”
E-commerce giant eBay has also found success using a graph-powered suggestion engine, in this case, for a sophisticated real-time courier/package routing solution.
Senior Developer Volker Pacher at eBay says his team found a graph database “to be literally thousands of times faster than our prior MySQL solution, with queries that require 10-100 times less code. Today, our graph database provides eBay with functionality that was previously impossible.”
Storing and querying recommendation data using a graph database allows your application to provide real-time results rather than precalculated, stale data.
As consumer expectations increase – and their patience decreases – providing these sorts of relevant, real-time suggestions will become a greater competitive advantage than ever before.
Download your copy of this white paper, The Top 5 Use Cases of Graph Databases, and discover how to tap into the power of connected data at your enterprise.
Catch up with the rest of the “Graph Databases in the Enterprise” series:
About the Author
Jim Webber & Ian Robinson, Chief Scientist & Senior Engineer
Jim Webber is Chief Scientist at Neo Technology working on next-generation solutions for massively scaling graph data. Prior to joining Neo Technology, Jim was a Professional Services Director with ThoughtWorks where he worked on large-scale computing systems in finance and telecoms. Jim has a Ph.D. in Computing Science from the Newcastle University, UK.
Ian Robinson is an Senior Engineer at Neo Technology. He is a co-author of ‘REST in Practice’ (O’Reilly) and a contributor to the forthcoming books ‘REST: From Research to Practice’ (Springer) and ‘Service Design Patterns’ (Addison-Wesley). He presents at conferences worldwide on the big Web graph of REST, and the awesome graph capabilities of Neo4j.