Practical Recommender Systems

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. SummaryOnline 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!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 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 BookPractical 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 insideHow to collect and understand user behaviorCollaborative and content-based filteringMachine learning algorithms Real-world 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 SYSTEMSWhat is a recommender? User behavior and how to collect it Monitoring the system Ratings and how to calculate themNon-personalized recommendationsThe user (and content) who came in from the coldPART 2 - RECOMMENDER ALGORITHMSFinding similarities among users and among contentCollaborative filtering in the neighborhoodEvaluating and testing your recommenderContent-based filteringFinding hidden genres with matrix factorizationTaking the best of all algorithms: implementing hybrid recommendersRanking and learning to rankFuture of recommender systems 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. 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. What's inside>/p> 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 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 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

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!