Recommender Systems
Recommender Systems
Markus Zanker, Alpen-Adria Universität Klagenfurt, Austria
Alexander Felfernig, Technische Universität Graz, Austria
Gerhard Friedrich, Alpen-Adria Universität Klagenfurt, Austria
$89.99
USDIn 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
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, Distinguished McKnight Professor, University of Minnesota
‘This book is the most comprehensive and useful introductory text on recommender systems yet published. Anyone who teaches in this area will be pleased to have, at last, one source for all of the essential topics. Especially noteworthy are the book's broad coverage of different types of recommenders and its thorough treatment of evaluation. It is billed as an introduction, but I think that even experienced practitioners will find new and interesting material here.' Robin Burke
Product details
September 2010Hardback
9780521493369
352 pages
231 × 155 × 25 mm
0.6kg
72 b/w illus. 29 tables
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.
