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Practical 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! Practica...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Falk, Kim (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Shelter Island, NY : Manning Publications, [2019]
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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245 1 0 |a Practical recommender systems /  |c Kim Falk. 
264 1 |a Shelter Island, NY :  |b Manning Publications,  |c [2019] 
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300 |a 1 online resource (1 volume) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
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588 0 |a Online resource; title from title page (Safari, viewed March 27, 2019). 
504 |a Includes bibliographical references and index. 
520 |a 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. 
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650 0 |a Recommender systems (Information filtering) 
650 0 |a Data mining. 
650 0 |a Web sites  |x Design. 
650 0 |a Human-computer interaction. 
650 2 |a Data Mining 
650 6 |a Systèmes de recommandation (Filtrage d'information) 
650 6 |a Exploration de données (Informatique) 
650 6 |a Sites Web  |x Conception. 
650 7 |a Data mining  |2 fast 
650 7 |a Human-computer interaction  |2 fast 
650 7 |a Recommender systems (Information filtering)  |2 fast 
650 7 |a Web sites  |x Design  |2 fast 
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