Practical Recommender Systems

Summary

Online recommender systems help users find movies, jobs, restaurants-even romance There's an art in combining statistics, demographics, and query terms to achieve results that will delight them. Learn to build a recommender system the right way: it can make or break your application

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Using behavioral and demographic data, these systems make predictions about what users will be most interested in at a particular time, resulting in high-quality, ordered, personalized suggestions. Recommender systems are practically a necessity for keeping your site content current, useful, and interesting to your visitors.

About the Book

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.

What's inside

How to collect and understand user behaviorCollaborative and content-based filteringMachine learning algorithms Real-world examples in Python About the Reader

Readers need intermediate programming and database skills.

About the Author

Kim Falk is an experienced data scientist who works daily with machine learning and recommender systems.

Table of Contents

PART 1 - GETTING READY FOR RECOMMENDER SYSTEMSWhat is a recommender? User behavior and how to collect it Monitoring the system Ratings and how to calculate themNon-personalized recommendationsThe user (and content) who came in from the coldPART 2 - RECOMMENDER ALGORITHMSFinding similarities among users and among contentCollaborative filtering in the neighborhoodEvaluating and testing your recommenderContent-based filteringFinding hidden genres with matrix factorizationTaking the best of all algorithms: implementing hybrid recommendersRanking and learning to rankFuture of recommender systems