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Recommender Systems, by Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich
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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.
- Sales Rank: #415873 in eBooks
- Published on: 2010-09-30
- Released on: 2013-07-24
- Format: Kindle eBook
Review
'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
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 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.
Most helpful customer reviews
11 of 12 people found the following review helpful.
The Best Introductory Book about Recommendations
By H. Lee
I am referring to this book for my lectures and this book is fascinating.
First of all, this book is really well-written. The authors explained recommendation technology concepts in an easy-to-follow way. At the same time, they didn't miss the most up-to-date research topics.
Second, all the writers are world's leading researchers of recommendations.
Therefore, they are good to lead readers to more focal points. I like the bibliographic notes at the end of each chapter.
Lastly, by introducing toy examples in every chapter, readers are able to easily comprehend each technology. Actually, this part is my favorite. I think this book is between a reference book of recommendation-related rationales and practical programming books like Manning series or O'Reilly's.
I already made my mind to choose this book as a textbook of my course in the next semester.
4 of 4 people found the following review helpful.
Best introduction
By Eduardo X
I've been studying recomender systems for my Bsc degree in the last two years and I've spent lots of money reading many other books. This book is the cheaper one on my library and I'm so pleased about the content and the way the authors explain things. I know most people doesn't like math and I believe you shouldn't read about this subject but, even if you math weaker, you can have this book.
I'm saying that because I had other books, for example:
http://www.amazon.com/gp/product/3540720782/ref=oh_details_o00_s00_i01
http://www.amazon.com/gp/product/3790821330/ref=oh_details_o01_s00_i02
http://www.amazon.com/gp/product/1933988665/ref=oh_details_o03_s00_i03
Those ones are good but didn't fulfill my desires.
Besides, it is easy to read and understand the concepts, I felt it gave me a good understand of the recomender system universe. I agree that this is the first step. But this first step is so dense that the second step seems so far away.
3 of 4 people found the following review helpful.
Too much e-commerce
By Sonne Im Herz
Well, I am new to the field of recommender system,
so I bought this book as the golden standard.
It is well written and quite interesting,
but for my taste it is too
theoretical and with too much focus
on e-commerce-recommenders and even customer-decision-making.
The book could have more information for that price.
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