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 book introduces different approaches to developing recommender systems that automate choice-making strategies to provide affordable, personal, and high-quality recommendations.

About the Author
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 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 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 is a chaired Professor at the Alpen-Adria Universitat 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.

Reviews
'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



Book Information
ISBN 9780521493369
Author Dietmar Jannach
Format Hardback
Page Count 352
Imprint Cambridge University Press
Publisher Cambridge University Press
Weight(grams) 600g
Dimensions(mm) 231mm * 155mm * 25mm