Recommender Systems

An Introduction

Nonfiction, Computers, Database Management, General Computing, Internet
Cover of the book Recommender Systems by Dietmar  Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich, Cambridge University Press
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Author: Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich ISBN: 9781139635943
Publisher: Cambridge University Press Publication: September 30, 2010
Imprint: Cambridge University Press Language: English
Author: Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich
ISBN: 9781139635943
Publisher: Cambridge University Press
Publication: September 30, 2010
Imprint: Cambridge University Press
Language: English

In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.

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