Building Recommendation Systems in Python and JAX

Preface

How did you come to find this book? Did you see an ad for it on a website? Maybe a friend or mentor suggested it; or perhaps you saw a post on social media that referenced it. Could it be that you found it sitting on a shelf in a bookstore—a bookstore that your trusty maps app led you to? However you came to find it, you’ve almost certainly come to this book via a recommendation system.

Implementing and designing systems that provide suggestions to users is among the most popular and most essential applications of machine learning (ML) to any business. Whether you want to help your users find the best clothing to match their tastes, the most appealing items to buy from an online store, videos to enrich and entertain them, maximally engaging content to surface from their networks, or the news highlights they need to know on that day, recommendation systems provide the way.

Modern recommendation system designs are as diverse as the domains they serve. These systems consist of the computer software architectures to implement and execute product goals, in addition to the algorithmic components of ranking. Methods for ranking recommendations can come from traditional statistical learning algorithms, linear-algebraic inspirations, geometric considerations, and, of course, gradient-based methods. Just as the algorithmic methods are diverse, so too are the modeling and evaluation considerations for recommending: personalized ranking, search recommendations, sequence modeling, ...