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Beschreibung
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.
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.
Über den Autor
Dietmar Jannach is a chaired Professor of Computer Science at TU Dortmund, Germany. The author of more than 100 scientific papers, he is a member of the editorial board of the Applied Intelligence journal and the review board of the International Journal of Electronic Commerce.
Inhaltsverzeichnis
1. Introduction; Part I. Introduction into Basic Concepts: 2. Collaborative recommendation; 3. Content-based recommendation; 4. Knowledge-based recommendation; 5. Hybrid recommendation approaches; 6. Explanations in recommender systems; 7. Evaluating recommender systems; 8. Case study - personalized game recommendations on the mobile Internet; Part II. Recent Developments: 9. Attacks on collaborative recommender systems; 10. Online consumer decision making; 11. Recommender systems and the next-generation Web; 12. Recommendations in ubiquitous environments; 13. Summary and outlook.
Details
Erscheinungsjahr: 2010
Genre: Importe, Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
ISBN-13: 9780521493369
ISBN-10: 0521493366
Sprache: Englisch
Einband: Gebunden
Autor: Jannach, Dietmar
Zanker, Markus
Felfernig, Alexander
Hersteller: Cambridge University Press
Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de
Maße: 235 x 157 x 23 mm
Von/Mit: Dietmar Jannach (u. a.)
Erscheinungsdatum: 01.09.2010
Gewicht: 0,661 kg
Artikel-ID: 107437011

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