Kickstart Your Recommender System

Hosted by Katerina Zanos

82 students

In this video

What you'll learn

Understand the core building blocks of a recommender system

and how metrics connect them.

Use metrics to guide recommender system design

and shape architecture and engineering decisions

Build a metrics stack that reflects your product goal

and move from vague objectives to concrete levers you can pull.

Why this topic matters

In modern recommender systems, metrics are at the steering wheel, not just the dashboard. If you don’t choose them deliberately, a single metric will quietly take over and push your system into behaviors you never intended. This session starts from recommender first principles, then shows the first step to designing your system with a metrics-first approach.

You'll learn from

Katerina Zanos

Principal Machine Learning Engineer, Disney+/ESPN at The Walt Disney Company

Katerina Zanos is a Principal Machine Learning Engineer at The Walt Disney Company, where she leads work on personalization for ESPN. Over the past decade, she’s shipped recommender systems and ranking models at The New York Times, Meta (Feed & Reels), and now Disney/ESPN, working end-to-end from product metrics and experimentation to large-scale ML infrastructure. With a background in both journalism and engineering, Katerina focuses on building recommendation systems for media products that are not only high-performing, but grounded in clear objectives and thoughtful product design.