Recommender Systems: An Introduction

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.

Dietmar Jannach is a Chaired Professor of computer science at Technische Universitt Dortmund, Germany. The author of more than one hundred 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 Assistant Professor in the Department for Applied Informatics and the director of the study program Information Management at Alpen-Adria Universitt Klagenfurt, Austria. He is an associate editor of the International Journal of Human-Computer Studies and cofounder and director of ConfigWorks GmbH. Alexander Felfering is University Professor at Technische Universitt Graz Austria. His research in recommender and configuration systems was honored in 2009 with the Heinz Zemanek Award. Felfering has published more than 130 scientific papers, is a review board member of the International Journal of Electronic Commerce, and is a cofounder of ConfigWorks GmbH. Gerhard Friedrich is a Chaired Professor at Alpen-Adria Universitt 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 Al Communications and an associate editor of the International Journal of Mass Customisation.

Foreword	p. ix
Preface	p. xiii
Introduction	p. 1
Part I: Introduction to basic concepts	p. 2
Part II: Recent developments	p. 8
Introduction to Basic Concepts
Collaborative recommendation	p. 13
User-based nearest neighbor recommendation	p. 13
Item-based nearest neighbor recommendation	p. 18
About ratings	p. 22
Further model-based and preprocessing-based approaches	p. 26
Recent practical approaches and systems	p. 40
Discussion and summary	p. 47
Bibliographical notes	p. 49
Content-based recommendation	p. 51
Content representation and content similarity	p. 52
Similarity-based retrieval	p. 58
Other text classification methods	p. 63
Discussion	p. 74
Summary	p. 77
Bibliographical notes	p. 79
Knowledge-based recommendation	p. 81
Introduction	p. 81
Knowledge representation and reasoning	p. 82
Interacting with constraint-based recommenders	p. 87
Interacting with case-based recommenders	p. 101
Example applications	p. 113
Bibliographical notes	p. 122
Hybrid recommendation approaches	p. 124
Opportunities for hybridization	p. 125
Monolithic hybridization design	p. 129
Parallelized hybridization design	p. 134
Pipelined hybridization design	p. 138
Discussion and summary	p. 141
Bibliographical notes	p. 142
Explanations in recommender systems	p. 143
Introduction	p. 143
Explanations in constraint-based recommenders	p. 147
Explanations in case-based recommenders	p. 157
Explanations in collaborative filtering recommenders	p. 161
Summary	p. 165
Evaluating recommender systems	p. 166
Introduction	p. 166
General properties of evaluation research	p. 167
Popular evaluation designs	p. 175
Evaluation on historical datasets	p. 177
Alternate evaluation designs	p. 184
Summary	p. 187
Bibliographical notes	p. 188
Case study: Personalized game recommendations on the mobile Internet	p. 189
Application and personalization overview	p. 191
Algorithms and ratings	p. 193
Evaluation	p. 194
Summary and conclusions	p. 206
Recent Developments
Attacks on collaborative recommender systems	p. 211
A first example	p. 212
Attack dimensions	p. 213
Attack types	p. 214
Evaluation of effectiveness and countermeasures	p. 219
Countermeasures	p. 221
Privacy aspects - distributed collaborative filtering	p. 225
Discussion	p. 232
Online consumer decision making	p. 234
Introduction	p. 234
Context effects	p. 236
Primacy/recency effects	p. 240
Further effects	p. 243
Personality and social psychology	p. 245
Bibliographical notes	p. 252
Recommender systems and the next-generation web	p. 253
Trust-aware recommender systems	p. 254
Folksonomies and more	p. 262
Ontological filtering	p. 279
Extracting semantics from the web	p. 285
Summary	p. 288
Recommendations in ubiquitous environments	p. 289
Introduction	p. 289
Context-aware recommendation	p. 291
Application domains	p. 294
Summary	p. 297
Summary and outlook	p. 299
Summary	p. 299
Outlook	p. 300
Bibliography	p. 305
Index	p. 333
Table of Contents provided by Ingram. All Rights Reserved.

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Recommender Systems: An Introduction

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