Practical Recommender Systems

Kim Falk (Autor)

Buch | Softcover

432 Seiten

2019
Manning Publications (Verlag)
978-1-61729-270-5 (ISBN)

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Practical introduction to recommender system algorithms
Collaborative and content-based filtering
Creating individual recommendations from visitor data
Real-world examples of recommender systems

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.

Practical Recommender Systems goes behind the curtain to show you how recommender systems work and, more importantly, how to create and apply them for your site.

After you've covered the basics of how recommender systems work, you'll discover how to collect user data and produce personalized recommendations.

Next, you'll learn how and where to use the most popular recommendation algorithms and see examples of them in action on sites like Amazon and Netflix. Finally, this hands-on guide covers scaling problems and other issues you may encounter as your site grows.

This book assumes you're comfortable reading code in Python and have some experience with databases.

Kim Falk is a Data Scientist at Adform, where he is working on recommender systems. He has experience in providing recommendations for large entertainment companies and working with big data solutions.

Erscheinungsdatum	04.07.2018
Verlagsort	New York
Sprache	englisch
Gewicht	779 g
Einbandart	kartoniert
Themenwelt	Informatik ► Datenbanken ► Data Warehouse / Data Mining
	Mathematik / Informatik ► Informatik ► Theorie / Studium
	Mathematik / Informatik ► Informatik ► Web / Internet
	Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra
Schlagworte	Algorithmen • Data Mining • Data Science • Datenanalyse • Datenbanken • Empfehlungssysteme im E-Commerce • Online Advertisement • Online Marketing • Onlineshop • Python • user data logging • user generated data
ISBN-10	1-61729-270-2 / 1617292702
ISBN-13	978-1-61729-270-5 / 9781617292705
Zustand	Neuware