國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Recommender systems handbook

Ricci, Francesco.

Recommender systems handbook

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Recommender systems handbookedited 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)
電子資源:	http://dx.doi.org/10.1007/978-1-4899-7637-6
ISBN:	9781489976376$q(electronic bk.)

Recommender systems handbook
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$q(electronic bk.)

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

310886
Recommender systems (Information filtering)

LC Class. No.: QA76.9.I58

Dewey Class. No.: 006.33

Recommender systems handbook
LDR:03554nmm a2200337 a 4500 001 476470
003 DE-He213
005 20160421144505.0
006 m d
007 cr nn 008maaau
008 160526s2015 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 310886
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Information Storage and Retrieval. $3 274190
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
700 1 $a Ricci, Francesco. $3 284973
700 1 $a Rokach, Lior. $3 253733
700 1 $a Shapira, Bracha. $3 353709
710 2 $a SpringerLink (Online service) $3 273601
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)