Recommender Systems: An Introduction

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9780521493369: Recommender Systems: An Introduction

This book offers an overview of approaches to developing state-of-the-art recommender systems that automate a variety of choice-making strategies with the goal of providing affordable, personal, and high-quality recommendations. The authors present algorithmic approaches for generating personalized buying proposals, as well as more interactive and knowledge-based approaches. They discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies.

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'Behind the modest title of 'An Introduction' lies the type of work the field needs to consolidate its learning and move forward to address new challenges. Across the chapters that follow lie both a tour of what the field knows well - a diverse collection of algorithms and approaches to recommendation - and a snapshot of where the field is today as new approaches derived from social computing and the semantic web find their place in the recommender systems toolbox. Let's all hope this worthy effort spurs yet more creativity and innovation to help recommender systems move forward to new heights.' Joseph A. Konstan, from the Foreword

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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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Dietmar Jannach, Markus Zanker, Alexander Felfernig
Editorial: CAMBRIDGE UNIVERSITY PRESS, United Kingdom (2011)
ISBN 10: 0521493366 ISBN 13: 9780521493369
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Descripción CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2011. Hardback. Estado de conservación: New. 229 x 155 mm. Language: English . Brand New Book. 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. Nº de ref. de la librería AAZ9780521493369

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Descripción Cambridge University Press, 2010. HRD. Estado de conservación: New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Nº de ref. de la librería GB-9780521493369

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Descripción Cambridge University Press. Hardback. Estado de conservación: new. BRAND NEW, Recommender Systems: An Introduction, Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich, 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. Nº de ref. de la librería B9780521493369

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Dietmar Jannach, Markus Zanker, Alexander Felfernig
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Descripción Cambridge University Press 2010-09-30, Cambridge, 2010. hardback. Estado de conservación: New. Nº de ref. de la librería 9780521493369

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Dietmar Jannach, Markus Zanker, Alexander Felfernig
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Descripción CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2011. Hardback. Estado de conservación: New. 229 x 155 mm. Language: English . Brand New Book. 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. Nº de ref. de la librería AAZ9780521493369

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Descripción Hardback. Estado de conservación: New. Not Signed; 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. book. Nº de ref. de la librería ria9780521493369_rkm

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Descripción Cambridge University Press, 2010. HRD. Estado de conservación: New. New Book. Shipped from US within 10 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Nº de ref. de la librería I1-9780521493369

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Descripción Cambridge University Press, 2010. Hardcover. Estado de conservación: Brand New. 1st edition. 360 pages. 9.00x6.10x1.00 inches. In Stock. Nº de ref. de la librería __0521493366

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