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 behavior
- Collaborative and content-based filtering
- Machine learning algorithms
- Real-world examples in Python
About the Reader
Readers need intermediate programming and database skills.
Table of Contents
- PART 1 - GETTING READY FOR RECOMMENDER SYSTEMS
- What is a recommender?
- User behavior and how to collect it
- Monitoring the system
- Ratings and how to calculate them
- Non-personalized recommendations
- The user (and content) who came in from the cold
- PART 2 - RECOMMENDER ALGORITHMS
- Finding similarities among users and among content
- Collaborative filtering in the neighborhood
- Evaluating and testing your recommender
- Content-based filtering
- Finding hidden genres with matrix factorization
- Taking the best of all algorithms: implementing hybrid recommenders
- Ranking and learning to rank
- Future of recommender systems
About the Author
Kim Falk is a Data Scientist at Adform, where he is working on recommender systems. He has experience in providing recommendations for large entertainment companies and working with big data solutions.
