Kim Falk

KIM FALK is a data scientist who is experienced building data-driven applications. He’s passionate about recommender systems and machine learning in general. He has trained recommender systems to provide movie choices to end users as well as ads to people, and has even helped attorneys find case law content. He’s worked with Big Data solutions and machine learning since 2010. Kim often speaks and writes about recommender systems. You can find him at http://kimfalk.org.

When he isn’t teaching machines to stalk people, Kim is a family man, father, and trail runner with his German Pointer.

books by Kim Falk

Practical Recommender Systems
  • January 2019
  • ISBN 9781617292705
  • 432 pages
Included with a Manning Online subscription
  • printed in black & white
  • Available translations: Korean, Polish, Russian, Simplified Chinese
catalog / Data Science / Data Engineering / Recommender Systems

Practical Recommender Systems explains how recommender systems work and shows how to create and apply them for your site. After covering the basics, you’ll see how to collect user data and produce personalized recommendations. You’ll learn how to use the most popular recommendation algorithms and see examples of them in action on sites like Amazon and Netflix. Finally, the book covers scaling problems and other issues you’ll encounter as your site grows.