The Practitioner's Guide to Graph Data

Chapter 12. Recommendations in Production

Pretty much every application you use these days has a “recommended for you” section.

Just think about your favorite applications for digital media, apparel, or retail providers. We rely on the recommendation pane in our media apps to find new movies to watch or books to read. Brands like Nike tailor your in-app experience with personal and customized wardrobes. Even your local grocery store’s app delivers recommended coupons to you for your next visit.

Recommendations and personalization have infiltrated almost every nook and cranny of our digital experience.

But how do you build a process that delivers recommendations within an application at the speed that we have all learned to expect?

As we walked through in Chapter 10, it is very possible to connect data sources with a graph and create personalized recommendations for a user. However, the sheer amount of data that is required to process a graph-based recommendation at scale significantly limits how you would use collaborative filtering within a production application.

We don’t think a user of Nike’s apparel app is going to wait the multiple seconds required to process an end-to-end NPS-inspired collaborative-filtering graph query. And neither should you.

Instead, we encourage you to think like a production engineer. We want to set up procedures that prioritize the end user’s in-app experience and then figure out how to connect a longer running query, like a graph-based ...