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!

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.

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 SYSTEMS
1. What is a recommender?
2. User behavior and how to collect it
3. Monitoring the system
4. Ratings and how to calculate them
5. Non-personalized recommendations
6. The user (and content) who came in from the cold
PART 2 - RECOMMENDER ALGORITHMS
7. Finding similarities among users and among content
8. Collaborative filtering in the neighborhood
9. Evaluating and testing your recommender
10. Content-based filtering
11. Finding hidden genres with matrix factorization
12. Taking the best of all algorithms: implementing hybrid recommenders
13. Ranking and learning to rank
14. Future of recommender systems

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.