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! "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

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 -- 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