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