Practical Recommender Systems

Description

Recommender systems are practically a necessity for keeping a site's content current, useful, and interesting to visitors. Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Practical Recommender Systems goes behind the curtain to show readers how recommender systems work and, more importantly, how to create and apply them for their site. This hands-on guide covers scaling problems and other issues they may encounter as their site grows. 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