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.

• Foreword p. ix Joseph A. Konstan
• Preface p. xiii
• 1 Introduction p. 1
• 1.1 Part I: Introduction to basic concepts p. 2
• 1.2 Part II: Recent developments p. 8
• Part I Introduction to Basic Concepts
• 2 Collaborative recommendation p. 13
• 2.1 User-based nearest neighbor recommendation p. 13
• 2.2 Item-based nearest neighbor recommendation p. 18
• 2.3 About ratings p. 22
• 2.4 Further model-based and preprocessing-based approaches p. 26
• 2.5 Recent practical approaches and systems p. 40
• 2.6 Discussion and summary p. 47
• 2.7 Bibliographical notes p. 49
• 3 Content-based recommendation p. 51
• 3.1 Content representation and content similarity p. 52
• 3.2 Similarity-based retrieval p. 58
• 3.3 Other text classification methods p. 63
• 3.4 Discussion p. 74
• 3.5 Summary p. 77
• 3.6 Bibliographical notes p. 79
• 4 Knowledge-based recommendation p. 81
• 4.1 Introduction p. 81
• 4.2 Knowledge representation and reasoning p. 82
• 4.3 Interacting with constraint-based recommenders p. 87
• 4.4 Interacting with case-based recommenders p. 101
• 4.5 Example applications p. 113
• 4.6 Bibliographical notes p. 122
• 5 Hybrid recommendation approaches p. 124
• 5.1 Opportunities for hybridization p. 125
• 5.2 Monolithic hybridization design p. 129
• 5.3 Parallelized hybridization design p. 134
• 5.4 Pipelined hybridization design p. 138
• 5.5 Discussion and summary p. 141
• 5.6 Bibliographical notes p. 142
• 6 Explanations in recommender systems p. 143
• 6.1 Introduction p. 143
• 6.2 Explanations in constraint-based recommenders p. 147
• 6.3 Explanations in case-based recommenders p. 157
• 6.4 Explanations in collaborative filtering recommenders p. 161
• 6.5 Summary p. 165
• 7 Evaluating recommender systems p. 166
• 7.1 Introduction p. 166
• 7.2 General properties of evaluation research p. 167
• 7.3 Popular evaluation designs p. 175
• 7.4 Evaluation on historical datasets p. 177
• 7.5 Alternate evaluation designs p. 184
• 7.6 Summary p. 187
• 7.7 Bibliographical notes p. 188
• 8 Case study: Personalized game recommendations on the mobile Internet p. 189
• 8.1 Application and personalization overview p. 191
• 8.2 Algorithms and ratings p. 193
• 8.3 Evaluation p. 194
• 8.4 Summary and conclusions p. 206
• Part II Recent Developments
• 9 Attacks on collaborative recommender systems p. 211
• 9.1 A first example p. 212
• 9.2 Attack dimensions p. 213
• 9.3 Attack types p. 214
• 9.4 Evaluation of effectiveness and countermeasures p. 219
• 9.5 Countermeasures p. 221
• 9.6 Privacy aspects - distributed collaborative filtering p. 225
• 9.7 Discussion p. 232
• 10 Online consumer decision making p. 234
• 10.1 Introduction p. 234
• 10.2 Context effects p. 236
• 10.3 Primacy/recency effects p. 240
• 10.4 Further effects p. 243
• 10.5 Personality and social psychology p. 245
• 10.6 Bibliographical notes p. 252
• 11 Recommender systems and the next-generation web p. 253
• 11.1 Trust-aware recommender systems p. 254
• 11.2 Folksonomies and more p. 262
• 11.3 Ontological filtering p. 279
• 11.4 Extracting semantics from the web p. 285
• 11.5 Summary p. 288
• 12 Recommendations in ubiquitous environments p. 289
• 12.1 Introduction p. 289
• 12.2 Context-aware recommendation p. 291
• 12.3 Application domains p. 294
• 12.4 Summary p. 297
• 13 Summary and outlook p. 299
• 13.1 Summary p. 299
• 13.2 Outlook p. 300
• Bibliography p. 305
• Index p. 333