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Practical Recommender Systems
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SummaryOnline recommender systems help users find movies, jobs, restaurantseven romance! Theres 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 TechnologyRecommender 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 highquality, ordered, personalized suggestions. Recommender systems are practically a necessity for keeping your site content current, useful, and interesting to your visitors.About the BookPractical Recommender Systems explains how recommender systems work and shows how to create and apply them for your site. After covering the basics, youll see how to collect user data and produce personalized recommendations. Youll 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 youll encounter as your site grows.Whats insideHow to collect and understand user behavior Collaborative and contentbased filtering Machine learning algorithms Realworld examples in PythonAbout the ReaderReaders need intermediate programming and database skills.About the AuthorKim Falk is an experienced data scientist who works daily with machine learning and recommender systems.Table of ContentsPART 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 Nonpersonalized 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 Contentbased 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
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