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Recommender Systems
An Introduction

CAD$91.95 (P)

  • Date Published: September 2010
  • availability: Available
  • format: Hardback
  • isbn: 9780521493369

CAD$ 91.95 (P)

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About the Authors
  • 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.

    • This is the first introductory book on recommender systems
    • Describes state-of-the-art technology and future topics including various case studies
    • Features a Foreword by Joseph A. Konstan
    • Covers interactive and knowledge-based advisory solutions
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    Reviews & endorsements

    '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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    Product details

    • Date Published: September 2010
    • format: Hardback
    • isbn: 9780521493369
    • length: 352 pages
    • dimensions: 231 x 155 x 25 mm
    • weight: 0.64kg
    • contains: 72 b/w illus. 29 tables
    • availability: Available
  • Table of Contents

    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.

  • Authors

    Dietmar Jannach, Technische Universität Dortmund, Germany
    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.

    Markus Zanker, Alpen-Adria Universität Klagenfurt, Austria
    Markus Zanker is an associate professor at the Alpen-Adria University, Klagenfurt, Austria. He directs the research group on recommender systems and is the director of the study programme in information management. In 2010 he was the program co-chair of the 4th International ACM Conference on Recommender Systems. He has published numerous papers in the area of artificial intelligence focusing on recommender systems, consumer buying behavior and human factors. He is also an associate editor of the International Journal of Human-Computer Studies.

    Alexander Felfernig, Technische Universität Graz, Austria
    Alexander Felfernig is Professor of Applied Software Engineering at the Graz University of Technology (TU Graz). In his research he focuses on intelligent methods and algorithms supporting the development and maintenance of complex knowledge bases. Furthermore, Alexander is interested in the application of AI techniques in the software engineering context, for example, the application of decision and recommendation technologies to make software requirements engineering processes more effective. For his research he received the Heinz–Zemanek Award from the Austrian Computer Society in 2009.

    Gerhard Friedrich, Alpen-Adria Universität Klagenfurt, Austria
    Gerhard Friedrich is a chaired Professor at the Alpen-Adria Universität Klagenfurt, Austria, where he is head of the Institute of Applied Informatics and directs the Intelligent Systems and Business Informatics research group. He is an editor of AI Communications and an associate editor of the International Journal of Mass Customisation.

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