Ebook: Recommender Systems Handbook

The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments.

Recommender Systems Handbook, an edited volume, is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included.

Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.

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The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments.

Recommender Systems Handbook, an edited volume, is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included.

Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.

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The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments.

Recommender Systems Handbook, an edited volume, is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included.

Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.

Content:
Front Matter....Pages i-xxix
Front Matter....Pages 37-37
Data Mining Methods for Recommender Systems....Pages 39-71
Content-based Recommender Systems: State of the Art and Trends....Pages 73-105
A Comprehensive Survey of Neighborhood-based Recommendation Methods....Pages 107-144
Advances in Collaborative Filtering....Pages 145-186
Developing Constraint-based Recommenders....Pages 187-215
Context-Aware Recommender Systems....Pages 217-253
Front Matter....Pages 255-255
Evaluating Recommendation Systems....Pages 257-297
A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment....Pages 299-331
How to Get the Recommender Out of the Lab?....Pages 333-365
Matching Recommendation Technologies and Domains....Pages 367-386
Recommender Systems in Technology Enhanced Learning....Pages 387-415
Front Matter....Pages 417-417
On the Evolution of Critiquing Recommenders....Pages 419-453
Creating More Credible and Persuasive Recommender Systems: The Influence of Source Characteristics on Recommender System Evaluations....Pages 455-477
Designing and Evaluating Explanations for Recommender Systems....Pages 479-510
Usability Guidelines for Product Recommenders Based on Example Critiquing Research....Pages 511-545
Map Based Visualization of Product Catalogs....Pages 547-576
Front Matter....Pages 577-577
Communities, Collaboration, and Recommender Systems in Personalized Web Search....Pages 579-614
Social Tagging Recommender Systems....Pages 615-644
Trust and Recommendations....Pages 645-675
Introduction to Recommender Systems Handbook....Pages 1-35
Front Matter....Pages 577-577
Group Recommender Systems: Combining Individual Models....Pages 677-702
Front Matter....Pages 703-703
Aggregation of Preferences in Recommender Systems....Pages 705-734
Active Learning in Recommender Systems....Pages 735-767
Multi-Criteria Recommender Systems....Pages 769-803
Robust Collaborative Recommendation....Pages 805-835
Back Matter....Pages 837-842

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The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments.

Recommender Systems Handbook, an edited volume, is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included.

Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.

Content:
Front Matter....Pages i-xxix
Front Matter....Pages 37-37
Data Mining Methods for Recommender Systems....Pages 39-71
Content-based Recommender Systems: State of the Art and Trends....Pages 73-105
A Comprehensive Survey of Neighborhood-based Recommendation Methods....Pages 107-144
Advances in Collaborative Filtering....Pages 145-186
Developing Constraint-based Recommenders....Pages 187-215
Context-Aware Recommender Systems....Pages 217-253
Front Matter....Pages 255-255
Evaluating Recommendation Systems....Pages 257-297
A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment....Pages 299-331
How to Get the Recommender Out of the Lab?....Pages 333-365
Matching Recommendation Technologies and Domains....Pages 367-386
Recommender Systems in Technology Enhanced Learning....Pages 387-415
Front Matter....Pages 417-417
On the Evolution of Critiquing Recommenders....Pages 419-453
Creating More Credible and Persuasive Recommender Systems: The Influence of Source Characteristics on Recommender System Evaluations....Pages 455-477
Designing and Evaluating Explanations for Recommender Systems....Pages 479-510
Usability Guidelines for Product Recommenders Based on Example Critiquing Research....Pages 511-545
Map Based Visualization of Product Catalogs....Pages 547-576
Front Matter....Pages 577-577
Communities, Collaboration, and Recommender Systems in Personalized Web Search....Pages 579-614
Social Tagging Recommender Systems....Pages 615-644
Trust and Recommendations....Pages 645-675
Introduction to Recommender Systems Handbook....Pages 1-35
Front Matter....Pages 577-577
Group Recommender Systems: Combining Individual Models....Pages 677-702
Front Matter....Pages 703-703
Aggregation of Preferences in Recommender Systems....Pages 705-734
Active Learning in Recommender Systems....Pages 735-767
Multi-Criteria Recommender Systems....Pages 769-803
Robust Collaborative Recommendation....Pages 805-835
Back Matter....Pages 837-842
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