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9780521493369

Recommender Systems: An Introduction

by
  • ISBN13:

    9780521493369

  • ISBN10:

    0521493366

  • Format: Hardcover
  • Copyright: 2010-09-30
  • Publisher: Cambridge University Press

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Summary

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.

Author Biography

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.

Table of Contents

Forewordp. ix
Prefacep. xiii
Introductionp. 1
Part I: Introduction to basic conceptsp. 2
Part II: Recent developmentsp. 8
Introduction to Basic Concepts
Collaborative recommendationp. 13
User-based nearest neighbor recommendationp. 13
Item-based nearest neighbor recommendationp. 18
About ratingsp. 22
Further model-based and preprocessing-based approachesp. 26
Recent practical approaches and systemsp. 40
Discussion and summaryp. 47
Bibliographical notesp. 49
Content-based recommendationp. 51
Content representation and content similarityp. 52
Similarity-based retrievalp. 58
Other text classification methodsp. 63
Discussionp. 74
Summaryp. 77
Bibliographical notesp. 79
Knowledge-based recommendationp. 81
Introductionp. 81
Knowledge representation and reasoningp. 82
Interacting with constraint-based recommendersp. 87
Interacting with case-based recommendersp. 101
Example applicationsp. 113
Bibliographical notesp. 122
Hybrid recommendation approachesp. 124
Opportunities for hybridizationp. 125
Monolithic hybridization designp. 129
Parallelized hybridization designp. 134
Pipelined hybridization designp. 138
Discussion and summaryp. 141
Bibliographical notesp. 142
Explanations in recommender systemsp. 143
Introductionp. 143
Explanations in constraint-based recommendersp. 147
Explanations in case-based recommendersp. 157
Explanations in collaborative filtering recommendersp. 161
Summaryp. 165
Evaluating recommender systemsp. 166
Introductionp. 166
General properties of evaluation researchp. 167
Popular evaluation designsp. 175
Evaluation on historical datasetsp. 177
Alternate evaluation designsp. 184
Summaryp. 187
Bibliographical notesp. 188
Case study: Personalized game recommendations on the mobile Internetp. 189
Application and personalization overviewp. 191
Algorithms and ratingsp. 193
Evaluationp. 194
Summary and conclusionsp. 206
Recent Developments
Attacks on collaborative recommender systemsp. 211
A first examplep. 212
Attack dimensionsp. 213
Attack typesp. 214
Evaluation of effectiveness and countermeasuresp. 219
Countermeasuresp. 221
Privacy aspects - distributed collaborative filteringp. 225
Discussionp. 232
Online consumer decision makingp. 234
Introductionp. 234
Context effectsp. 236
Primacy/recency effectsp. 240
Further effectsp. 243
Personality and social psychologyp. 245
Bibliographical notesp. 252
Recommender systems and the next-generation webp. 253
Trust-aware recommender systemsp. 254
Folksonomies and morep. 262
Ontological filteringp. 279
Extracting semantics from the webp. 285
Summaryp. 288
Recommendations in ubiquitous environmentsp. 289
Introductionp. 289
Context-aware recommendationp. 291
Application domainsp. 294
Summaryp. 297
Summary and outlookp. 299
Summaryp. 299
Outlookp. 300
Bibliographyp. 305
Indexp. 333
Table of Contents provided by Ingram. All Rights Reserved.

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