This nightmare scenario haunts e-commerce giants and streaming platforms alike.

While your model might boast impressive accuracy metrics in the lab, the harsh reality is that 76% of users ignore recommendations that don't immediately resonate with their interests.

But what if you could safely test new recommendation models in production without risking user experience or revenue?

Enter interleaving experiments – a battle-tested technique that giants like Netflix and Amazon use to validate their recommendation systems.

By cleverly mixing recommendations from both current and new models, this approach lets you measure real user engagement while minimizing risk.

In this guide, you'll discover how to implement interleaving experiments to evaluate recommendation models in production, protect your revenue, and ensure your recommendations actually drive user engagement.

Whether you're a data scientist, ML engineer, or technical lead, you'll learn the exact steps to validate your models with real users before full deployment.

Testing Recommendation Models in Production: A Deep Dive into Interleaving Experiments

Juan Carlos Olamendy