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Recommender Systems Handbook

Ricci Francesco Rokach Lior Shapira Bracha
Heftet / 2016 / Engelsk

Produktdetaljer

ISBN
9781489977809
Publisert
2016-08-23
Utgave
2. utgave
Utgiver
Vendor
Springer-Verlag New York Inc.
Høyde
235 mm
Bredde
155 mm
Aldersnivå
Research, P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet

Redaktør
Ricci Francesco
Rokach Lior
Shapira Bracha

Om bidragsyterne

Francesco Ricci is a professor of computer science at the Free University of Bozen-Bolzano, Italy. His current research interests include recommender systems, intelligent interfaces, mobile systems, machine learning, case-based reasoning, and the applications of ICT to health and tourism. He has published more than one hundred thirty of academic papers on these topics. He is the editor in chief of the Journal of Information Technology & Tourism and on the editorial board of User Modeling and User Adapted Interaction. Lior Rokach is a data scientist and an associate professor of information systems and software engineering at Ben-Gurion University of the Negev (BGU). Rokach established the machine learning laboratory in BGU which promotes innovative adaptations of machine learning and data mining methods to create the next generation of intelligent systems. Rokach is known for his contributions to the advancement of machine learning, recommender systems and cyber security. Bracha Shapira is an associate professor and the head of the information systems and engineering Department at Ben-Gurion University of the Negev (BGU). She leads large scale research projects at the Telekom Innovation Laboratories at BGU in the area of data analytics, recommender systems and personalization that delivers innovative technologies to address challenges in these fields. Shapira is known for her contribution in integrating social network, context awareness and privacy consideration to recommender systems.

Recommender Systems Handbook

Ricci Francesco Rokach Lior Shapira Bracha
Heftet / 2016 / Engelsk
Ricci Francesco Rokach Lior Shapira Bracha
Heftet / 2016 / Engelsk
Nettpris:
3.580,-
Levering 7-20 dager
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<p>“If you have time for just one book to get yourself up to speed with the latest and best in recommender systems, this is the book you want. … this is an excellent educational resource on the main techniques employed for making recommendations … . is definitely a book to read to get updated on the state of the art of recommender systems, and also to get a feel of the breadth of the research areas available in this area.” (Jun-Ping Ng, Computing Reviews, April, 2016)</p>

This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.
Les mer
Recommender Systems: Introduction and Challenges.- A Comprehensive Survey of Neighborhood-based Recommendation Methods.- Advances in Collaborative Filtering.- Semantics-aware Content-based Recommender Systems.- Constraint-based Recommender Systems.- Context-Aware Recommender Systems.- Data Mining Methods for Recommender Systems.- Evaluating Recommender Systems.- Evaluating Recommender Systems with User Experiments.- Explaining Recommendations: Design and Evaluation.- Recommender Systems in Industry: A Netflix Case Study.- Panorama of Recommender Systems to Support Learning.- Music Recommender Systems.- The Anatomy of Mobile Location-Based Recommender Systems.- Social Recommender Systems.- People-to-People Reciprocal Recommenders.- Collaboration, Reputation and Recommender Systems in Social Web Search.- Human Decision Making and Recommender Systems.- Privacy Aspects of Recommender Systems.- Source Factors in Recommender System Credibility Evaluation.- Personality and Recommender Systems.- Group Recommender Systems: Aggregation, Satisfaction and Group Attributes.- Aggregation Functions for Recommender Systems.- Active Learning in Recommender Systems.- Multi-Criteria Recommender Systems.- Novelty and Diversity in Recommender Systems.- Cross-domain Recommender Systems.- Robust Collaborative Recommendation.
Les mer
Includes major updates as well as 20 new chapters Presents detailed case studies Shares tips and insights from renowned experts in the field
GPSR Compliance The European Union's (EU) General Product Safety Regulation (GPSR) is a set of rules that requires consumer products to be safe and our obligations to ensure this. If you have any concerns about our products you can contact us on ProductSafety@springernature.com. In case Publisher is established outside the EU, the EU authorized representative is: Springer Nature Customer Service Center GmbH Europaplatz 3 69115 Heidelberg, Germany ProductSafety@springernature.com
Les mer

Relaterte produkter

<p>“If you have time for just one book to get yourself up to speed with the latest and best in recommender systems, this is the book you want. … this is an excellent educational resource on the main techniques employed for making recommendations … . is definitely a book to read to get updated on the state of the art of recommender systems, and also to get a feel of the breadth of the research areas available in this area.” (Jun-Ping Ng, Computing Reviews, April, 2016)</p>

This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.
Les mer
Recommender Systems: Introduction and Challenges.- A Comprehensive Survey of Neighborhood-based Recommendation Methods.- Advances in Collaborative Filtering.- Semantics-aware Content-based Recommender Systems.- Constraint-based Recommender Systems.- Context-Aware Recommender Systems.- Data Mining Methods for Recommender Systems.- Evaluating Recommender Systems.- Evaluating Recommender Systems with User Experiments.- Explaining Recommendations: Design and Evaluation.- Recommender Systems in Industry: A Netflix Case Study.- Panorama of Recommender Systems to Support Learning.- Music Recommender Systems.- The Anatomy of Mobile Location-Based Recommender Systems.- Social Recommender Systems.- People-to-People Reciprocal Recommenders.- Collaboration, Reputation and Recommender Systems in Social Web Search.- Human Decision Making and Recommender Systems.- Privacy Aspects of Recommender Systems.- Source Factors in Recommender System Credibility Evaluation.- Personality and Recommender Systems.- Group Recommender Systems: Aggregation, Satisfaction and Group Attributes.- Aggregation Functions for Recommender Systems.- Active Learning in Recommender Systems.- Multi-Criteria Recommender Systems.- Novelty and Diversity in Recommender Systems.- Cross-domain Recommender Systems.- Robust Collaborative Recommendation.
Les mer
Includes major updates as well as 20 new chapters Presents detailed case studies Shares tips and insights from renowned experts in the field
GPSR Compliance The European Union's (EU) General Product Safety Regulation (GPSR) is a set of rules that requires consumer products to be safe and our obligations to ensure this. If you have any concerns about our products you can contact us on ProductSafety@springernature.com. In case Publisher is established outside the EU, the EU authorized representative is: Springer Nature Customer Service Center GmbH Europaplatz 3 69115 Heidelberg, Germany ProductSafety@springernature.com
Les mer

Produktdetaljer

ISBN
9781489977809
Publisert
2016-08-23
Utgave
2. utgave
Utgiver
Vendor
Springer-Verlag New York Inc.
Høyde
235 mm
Bredde
155 mm
Aldersnivå
Research, P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet

Redaktør
Ricci Francesco
Rokach Lior
Shapira Bracha

Om bidragsyterne

Francesco Ricci is a professor of computer science at the Free University of Bozen-Bolzano, Italy. His current research interests include recommender systems, intelligent interfaces, mobile systems, machine learning, case-based reasoning, and the applications of ICT to health and tourism. He has published more than one hundred thirty of academic papers on these topics. He is the editor in chief of the Journal of Information Technology & Tourism and on the editorial board of User Modeling and User Adapted Interaction. Lior Rokach is a data scientist and an associate professor of information systems and software engineering at Ben-Gurion University of the Negev (BGU). Rokach established the machine learning laboratory in BGU which promotes innovative adaptations of machine learning and data mining methods to create the next generation of intelligent systems. Rokach is known for his contributions to the advancement of machine learning, recommender systems and cyber security. Bracha Shapira is an associate professor and the head of the information systems and engineering Department at Ben-Gurion University of the Negev (BGU). She leads large scale research projects at the Telekom Innovation Laboratories at BGU in the area of data analytics, recommender systems and personalization that delivers innovative technologies to address challenges in these fields. Shapira is known for her contribution in integrating social network, context awareness and privacy consideration to recommender systems.

Relaterte produkter