大葉大學圖書館 |

Recommender systems handbook[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.33
書名/作者:	Recommender systems handbook/ edited by Francesco Ricci, Lior Rokach, Bracha Shapira.
其他作者:	Ricci, Francesco.
出版者:	Boston, MA : : Springer US :, 2015.
面頁冊數:	xvii, 1003 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Recommender systems (Information filtering)
標題:	Computer Science.
標題:	Information Storage and Retrieval.
標題:	Artificial Intelligence (incl. Robotics)
ISBN:	9781489976376
ISBN:	9781489976369
內容註:	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.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-1-4899-7637-6

Recommender systems handbook[electronic resource] /
Recommender systems handbook[electronic resource] /edited by Francesco Ricci, Lior Rokach, Bracha Shapira. - 2nd ed. - Boston, MA :Springer US :2015. - xvii, 1003 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9781489976376

Standard No.: 10.1007/978-1-4899-7637-6doiSubjects--Topical Terms:

467318
Recommender systems (Information filtering)

LC Class. No.: QA76.9.I58

Dewey Class. No.: 006.33

Recommender systems handbook[electronic resource] /
LDR:03554nam a2200337 a 4500 001 443800
003 DE-He213
005 20160421144505.0
006 m d
007 cr nn 008maaau
008 160715s2015 mau s 0 eng d
020 $a 9781489976376 $q (electronic bk.)
020 $a 9781489976369 $q (paper)
024 7 $a 10.1007/978-1-4899-7637-6 $2 doi
035 $a 978-1-4899-7637-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.I58
072 7 $a UNH $2 bicssc
072 7 $a UND $2 bicssc
072 7 $a COM030000 $2 bisacsh
082 0 4 $a 006.33 $2 23
090 $a QA76.9.I58 $b R311 2015
245 0 0 $a Recommender systems handbook $h [electronic resource] / $c edited by Francesco Ricci, Lior Rokach, Bracha Shapira.
250 $a 2nd ed.
260 $a Boston, MA : $b Springer US : $b Imprint: Springer, $c 2015.
300 $a xvii, 1003 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Recommender systems (Information filtering) $3 467318
650 1 4 $a Computer Science. $3 423143
650 2 4 $a Information Storage and Retrieval. $3 464540
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 463642
700 1 $a Ricci, Francesco. $3 467931
700 1 $a Rokach, Lior. $3 469020
700 1 $a Shapira, Bracha. $3 411899
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-1-4899-7637-6
950 $a Computer Science (Springer-11645)