Imagine you're scrolling through your favorite streaming service, desperately seeking that perfect movie for your Friday night.

Frustrating, isn't it?

Now picture a world where your streaming app knows you better than your best friend, serving up the ideal film without a moment's hesitation.

But how do we know if these digital matchmakers are truly improving?

Enter interleaving experiments: an approach that's reshaping how we evaluate and refine recommender systems.

It offers a faster, more efficient way to identify better ML models.

In this article, we'll explore how this innovative technique is transforming user experiences across platforms, from e-commerce to content streaming.

Get ready to discover the future of personalization and learn why interleaving might just be the key to unlocking truly intuitive digital experiences.

Interleaving Experiments: Revolutionizing Recommender System Evaluation

Juan Carlos Olamendy