“You may also like” sounds simple, but there’s a lot happening behind the scenes.

Real-time recommendations work best when they take into account both the user’s needs (what is of interest to them) and your business strategy (items you need to promote).

The truly amazing thing is how real-time recommendation strategies are now being adopted by so many industries beyond retail, travel and entertainment. New industries finding the benefits of real-time recommendation engines include government services, financial services, healthcare and job recruiting.

What’s fueling these game-changing recommendations?

Graph technology. With Neo4j Graph Platform we have built a hybrid recommender framework that uses a score-based approach to provide the best-fit recommendations, by leveraging multiple techniques like collaborative filtering, content filtering, business rules and knowledge-based filtering.

Check out the infographic* below to learn how graph technology connects all of your data and enables you to use multiple methods to put you in control.

Read this infographic on real-time recommendations using graph technology.

*Also available in French and German!

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Level up your recommendation engine:
Learn why a recommender system built on graph technology is more powerful and efficient with this white paper, Powering Recommendations with Graph Databases – get your copy today.


Read the White Paper

 

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About the Author

Navneet Mathur , Senior Director of Global Solutions, Neo4j

Navneet Mathur Image

Nav Mathur is Senior Director of Global Solutions at Neo4j. He is responsible for solutions development and go-to-market activities.


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