Recommender Systems: An Introduction

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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 authors also 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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Editorial Reviews

From the Publisher
'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

  • ISBN-13: 9780521493369
  • Publisher: Cambridge University Press
  • Publication date: 9/30/2010
  • Pages: 352
  • Sales rank: 923,489
  • Product dimensions: 6.00 (w) x 9.10 (h) x 0.90 (d)

Meet 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.

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Table of Contents

Foreword Joseph A. Konstan ix

Preface xiii

1 Introduction 1

1.1 Part I: Introduction to basic concepts 2

1.2 Part II: Recent developments 8

Part I Introduction to Basic Concepts

2 Collaborative recommendation 13

2.1 User-based nearest neighbor recommendation 13

2.2 Item-based nearest neighbor recommendation 18

2.3 About ratings 22

2.4 Further model-based and preprocessing-based approaches 26

2.5 Recent practical approaches and systems 40

2.6 Discussion and summary 47

2.7 Bibliographical notes 49

3 Content-based recommendation 51

3.1 Content representation and content similarity 52

3.2 Similarity-based retrieval 58

3.3 Other text classification methods 63

3.4 Discussion 74

3.5 Summary 77

3.6 Bibliographical notes 79

4 Knowledge-based recommendation 81

4.1 Introduction 81

4.2 Knowledge representation and reasoning 82

4.3 Interacting with constraint-based recommenders 87

4.4 Interacting with case-based recommenders 101

4.5 Example applications 113

4.6 Bibliographical notes 122

5 Hybrid recommendation approaches 124

5.1 Opportunities for hybridization 125

5.2 Monolithic hybridization design 129

5.3 Parallelized hybridization design 134

5.4 Pipelined hybridization design 138

5.5 Discussion and summary 141

5.6 Bibliographical notes 142

6 Explanations in recommender systems 143

6.1 Introduction 143

6.2 Explanations in constraint-based recommenders 147

6.3 Explanations in case-based recommenders 157

6.4 Explanations in collaborative filtering recommenders 161

6.5 Summary 165

7 Evaluating recommender systems 166

7.1 Introduction 166

7.2 General properties of evaluation research 167

7.3 Popular evaluation designs 175

7.4 Evaluation on historical datasets 177

7.5 Alternate evaluation designs 184

7.6 Summary 187

7.7 Bibliographical notes 188

8 Case study: Personalized game recommendations on the mobile Internet 189

8.1 Application and personalization overview 191

8.2 Algorithms and ratings 193

8.3 Evaluation 194

8.4 Summary and conclusions 206

Part II Recent Developments

9 Attacks on collaborative recommender systems 211

9.1 A first example 212

9.2 Attack dimensions 213

9.3 Attack types 214

9.4 Evaluation of effectiveness and countermeasures 219

9.5 Countermeasures 221

9.6 Privacy aspects - distributed collaborative filtering 225

9.7 Discussion 232

10 Online consumer decision making 234

10.1 Introduction 234

10.2 Context effects 236

10.3 Primacy/recency effects 240

10.4 Further effects 243

10.5 Personality and social psychology 245

10.6 Bibliographical notes 252

11 Recommender systems and the next-generation web 253

11.1 Trust-aware recommender systems 254

11.2 Folksonomies and more 262

11.3 Ontological filtering 279

11.4 Extracting semantics from the web 285

11.5 Summary 288

12 Recommendations in ubiquitous environments 289

12.1 Introduction 289

12.2 Context-aware recommendation 291

12.3 Application domains 294

12.4 Summary 297

13 Summary and outlook 299

13.1 Summary 299

13.2 Outlook 300

Bibliography 305

Index 333

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