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The first hands-on Recommendation Systems book for real-world applications.
Implementing and designing systems that make suggestions to users are among the most popular and essential machine learning applications available. Whether you want customers to find the most appealing items at your online store, videos to enrich and entertain them, or news they need to know, recommendation systems (RecSys) provide the way.
In this practical book, authors Bryan Bischof and Hector Yee illustrate the core concepts and examples to help you create a RecSys for any industry or scale. You'll learn the math, ideas, and implementation details you need to succeed. This book includes the RecSys platform components, relevant MLOps tools in your stack, plus code examples and helpful suggestions in PySpark, SparkSQL, FastAPI, and Weights & Biases.
What readers are saying:
“Bryan and Hector have distilled decades of recommendation system advancements into a concise, yet practical guide. Bridging the gap between theory and application, their book is packed with easy-to-understand Python and JAX examples. This is an indispensable guide for RecSys practitioners at all levels, from novices to experts.”
- Eugene Yan, Applied Scientist, Amazon
"Bryan and Hector have created something special here, introducing concepts that take most people years to learn within the RecSys domain and then providing clear code examples that put them into practice. I wish I'd had this book when I started out on my RecSys journey."
- Even Oldridge, Director of Recommendation Systems, Nvidia
"This book takes a holistic approach to building recommender systems, synthesizing math, code, systems design, and business application. It covers all the nuances that practitioners need to consider to implement real world solutions. The intuitive examples using publicly available datasets enables the reader to turn abstract concepts into concrete learnings."
- Eric Colson, AI Advisor, Former Chief Algorithms Officer at Stitch Fix, Former VP of Data Science & Engineering at Netflix.
”Recommender systems are among the most impactful ML systems ever deployed: this book brilliantly navigates the balance between principled modeling, clear code examples and architectural best practices. A must read for practitioners aspiring to build real-world systems, not just train models.“
- Jacopo Tagliabue, co-founder of Bauplan and Adj. Prof of ML Systems at NYU , Co-creator of RecList and evalRS
“This is the RecSys book that I've wished to find for years. Building Production Recommendation Systems in Python and JAX cleared up many questions I had about real world recommendation systems that had remained frustrating itches I couldn't quite scratch.”
- Will Kurt, Author: Bayesian Statistics the Fun Way
“I think all the topics were wonderful, well explained and interesting.”
- Eric Schles, Principal Data Scientist at Johns Hopkins
Bryan Bischof
…leads AI at Hex, and is an adjunct professor in the Rutgers Masters of Business and Analytics program where he teaches Data Science. Previously, he was the Head of Data Science at Weights and Biases, where he built the DS, ML, and Data Engineering teams.
He has built recommendation systems for clothing (at Stitch Fix), recommendation systems for technical blog posts (at Weights and Biases), built the world’s first recommendation system for coffee (at Blue Bottle Coffee), and now is building recommendation systems for AI agents. His data visualization work appeared in the popular book The Day it Finally Happens by Mike Pearl. His Ph.D. is in pure mathematics.
Hector Yee
…is a Staff Software engineer at Google where he has worked on multiple projects including creating the first content based ranker on Image Search, the self driving car perception, and writing the YouTube recommender system. He has won a technical Emmy for his work on personalized video ranking technology. He has an M.S. in computer graphics.
