Articles liés à Practical Recommender Systems

Practical Recommender Systems - Couverture souple

Falk, Kim

 
9781617292705: Practical Recommender Systems
Couverture souple
ISBN 10 : 1617292702 ISBN 13 : 9781617292705
Editeur : Manning, 2019

Synopsis

Summary

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!

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.

About the Book

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.

What's inside

  • 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.

Table of Contents

    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
    7. 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

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos de l'auteur

Kim Falk is an experienced data scientist who works daily with machine learning and recommender systems.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Manning
Date d'édition
2019
Langue
anglais
ISBN 10 
1617292702
ISBN 13 
9781617292705
Reliure
Broché
Numéro d'édition
1
Nombre de pages
432
Coordonnées du fabricant
non disponible
Personne responsable
Simon and Schuster Netherlands BV, GPSR Europäische Union
info@simonandschuster.nl

Herculesplein 96
Utrecht
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