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  • 1
    UID:
    (DE-602)b3kat_BV025543269
    Format: ca. 350 S.
    ISBN: 9781441900470
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    (DE-605)HT011170077
    Format: 238 S. : Ill., graph. Darst.
    Series Statement: Decision support systems 27. 1999/[200],1/2
    Language: English
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  • 3
    Online Resource
    Online Resource
    New York : Springer
    UID:
    (DE-604)BV048214865
    Format: 1 Online-Ressource (XI, 1060 Seiten)
    Edition: Third Edition
    ISBN: 9781071621974
    Note: Korrektur durch Verlagsmeldung (September 2022). - Früheres Paket ZDB-2-SMA
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-1-07-162196-7
    Language: English
    Keywords: Empfehlungssystem ; Erfolgskontrolle ; Empfehlungssystem ; Mensch-Maschine-Kommunikation
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 4
    UID:
    (DE-605)HT018836829
    Format: 1 Online-Ressource (XVII, 1003 p. 144 illus., 78 illus. in color)
    Edition: 2nd ed. 2015
    ISBN: 9781489976376 , 9781489976369
    Language: English
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  • 5
    Online Resource
    Online Resource
    New York, NY : Springer US | Cham : Springer International Publishing AG
    UID:
    (DE-603)368075931
    Format: 1 Online-Ressource (XVII, 1003 Seiten) , 144 illus., 78 illus. in color.
    Edition: 2nd ed. 2015
    ISBN: 9781489976376 , 148997637X
    Additional Edition: Erscheint auch als Druck-Ausgabe Recommender Systems Handbook New York, NY : Springer US, 2015 9781489976369
    Additional Edition: 9781489976369
    Additional Edition: 9781489976383
    Additional Edition: 9781489977809
    Language: English
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  • 6
    Online Resource
    Online Resource
    New York, NY : Springer US | Cham : Springer International Publishing AG
    UID:
    (DE-603)494144459
    Format: 1 Online-Ressource (XI, 1060 Seiten) , 129 illus., 105 illus. in color.
    Edition: 3rd ed. 2022
    ISBN: 9781071621974 , 1071621971
    Additional Edition: Erscheint auch als Druck-Ausgabe Recommender Systems Handbook New York, NY : Springer US, 2022 9781071621967
    Additional Edition: 9781071621967
    Additional Edition: 9781071621981
    Additional Edition: 9781071621998
    Language: English
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  • 7
    UID:
    (DE-602)gbv_1800270518
    Format: 1 Online-Ressource (xi, 1060 Seiten) , Illustrationen, Diagramme
    Edition: Third edition
    ISBN: 9781071621974
    Series Statement: Springer eBook Collection
    Content: Preface -- Introduction -- Part 1: General Recommendation Techniques -- Trust Your Neighbors: A Comprehensive Survey of Neighborhood-based Methods for Recommender Systems (Desrosiers) -- Advances in Collaborative Filtering (Koren) -- Item Recommendation from Implicit Feedback (Rendle) -- Deep Learning for Recommender Systems (Zhang) -- Context Aware Re commender Sytems : From Foundatiom to Recent Developments (Bauman) -- Semantics and Content-based Recommendations (Musto) -- Part 2: Special Recommendation Techniques -- Session-based Recommender Systems (lannoch). -- Adversarial Recommender Systems: Attack, Defense, and Advances (Di Nola) -- Group Recommender Systems: Beyond Preferance Aggregation (Masthoff) -- People-to-People Reciprocal Recommenders (Koprinska) -- Natural Language Processing for Recommender Systems (Sar-Shalom) -- Design and Evaluation of Cross-domain Recommender Systems (Cremonesi) -- Part 3: Value and Impact of Recommender Systems -- Value and Impact of Recommender Systems (Zanker) -- Evaluating Recommender Systems (Shani) -- Novelty and Diversity in Recommender Systems (Castells) -- Multistakeholder Recommender Systems (Burke) -- Fairness in Recommender Systems (Ekstrand) -- Part 4: Human Computer Interaction -- Beyond Explaining Single Item Recommendations (Tintarev) -- Personality and Recommender Systems (Tkalčič) -- Individual and Group Decision Making and Recommender Systems (Jameson) -- Part 5: Recommender Systems Applications -- Social Recommender Systems (Guy) -- Food Recommender Systems (Trattner) -- Music Recommendation Systems: Techniques, Use Cases, and Challenges (Schedl) -- Multimedia Recommender Systems: Algorithms and Challenges (Deldjoo) -- Fashion Recommender Systems (Dokoohaki).
    Content: This third edition handbook describes in detail the classical methods as well as extensions and novel approaches that were more recently introduced. It consists of five parts: general recommendation techniques, special recommendation techniques, value and impact of recommender systems, human computer interaction, and applications. The first part presents the most popular and fundamental techniques currently used for building recommender systems, such as collaborative filtering, semantic-based methods, recommender systems based on implicit feedback, neural networks and context-aware methods. The second part of this handbook introduces more advanced recommendation techniques, such as session-based recommender systems, adversarial machine learning for recommender systems, group recommendation techniques, reciprocal recommenders systems, natural language techniques for recommender systems and cross-domain approaches to recommender systems. The third part covers a wide perspective to the evaluation of recommender systems with papers on methods for evaluating recommender systems, their value and impact, the multi-stakeholder perspective of recommender systems, the analysis of the fairness, novelty and diversity in recommender systems. The fourth part contains a few chapters on the human computer dimension of recommender systems, with research on the role of explanation, the user personality and how to effectively support individual and group decision with recommender systems. The last part focusses on application in several important areas, such as, food, music, fashion and multimedia recommendation. This informative third edition handbook provides a comprehensive, yet concise and convenient reference source to recommender systems for researchers and advanced-level students focused on computer science and data science. Professionals working in data analytics that are using recommendation and personalization techniques will also find this handbook a useful tool. .
    Additional Edition: ISBN 9781071621967
    Additional Edition: ISBN 9781071621998
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9781071621967
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9781071621981
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9781071621998
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 8
    UID:
    (DE-627)1800270518
    Format: 1 Online-Ressource (xi, 1060 Seiten) , Illustrationen, Diagramme
    Edition: Third edition
    ISBN: 9781071621974
    Series Statement: Springer eBook Collection
    Content: Preface -- Introduction -- Part 1: General Recommendation Techniques -- Trust Your Neighbors: A Comprehensive Survey of Neighborhood-based Methods for Recommender Systems (Desrosiers) -- Advances in Collaborative Filtering (Koren) -- Item Recommendation from Implicit Feedback (Rendle) -- Deep Learning for Recommender Systems (Zhang) -- Context Aware Re commender Sytems : From Foundatiom to Recent Developments (Bauman) -- Semantics and Content-based Recommendations (Musto) -- Part 2: Special Recommendation Techniques -- Session-based Recommender Systems (lannoch). -- Adversarial Recommender Systems: Attack, Defense, and Advances (Di Nola) -- Group Recommender Systems: Beyond Preferance Aggregation (Masthoff) -- People-to-People Reciprocal Recommenders (Koprinska) -- Natural Language Processing for Recommender Systems (Sar-Shalom) -- Design and Evaluation of Cross-domain Recommender Systems (Cremonesi) -- Part 3: Value and Impact of Recommender Systems -- Value and Impact of Recommender Systems (Zanker) -- Evaluating Recommender Systems (Shani) -- Novelty and Diversity in Recommender Systems (Castells) -- Multistakeholder Recommender Systems (Burke) -- Fairness in Recommender Systems (Ekstrand) -- Part 4: Human Computer Interaction -- Beyond Explaining Single Item Recommendations (Tintarev) -- Personality and Recommender Systems (Tkalčič) -- Individual and Group Decision Making and Recommender Systems (Jameson) -- Part 5: Recommender Systems Applications -- Social Recommender Systems (Guy) -- Food Recommender Systems (Trattner) -- Music Recommendation Systems: Techniques, Use Cases, and Challenges (Schedl) -- Multimedia Recommender Systems: Algorithms and Challenges (Deldjoo) -- Fashion Recommender Systems (Dokoohaki).
    Content: This third edition handbook describes in detail the classical methods as well as extensions and novel approaches that were more recently introduced. It consists of five parts: general recommendation techniques, special recommendation techniques, value and impact of recommender systems, human computer interaction, and applications. The first part presents the most popular and fundamental techniques currently used for building recommender systems, such as collaborative filtering, semantic-based methods, recommender systems based on implicit feedback, neural networks and context-aware methods. The second part of this handbook introduces more advanced recommendation techniques, such as session-based recommender systems, adversarial machine learning for recommender systems, group recommendation techniques, reciprocal recommenders systems, natural language techniques for recommender systems and cross-domain approaches to recommender systems. The third part covers a wide perspective to the evaluation of recommender systems with papers on methods for evaluating recommender systems, their value and impact, the multi-stakeholder perspective of recommender systems, the analysis of the fairness, novelty and diversity in recommender systems. The fourth part contains a few chapters on the human computer dimension of recommender systems, with research on the role of explanation, the user personality and how to effectively support individual and group decision with recommender systems. The last part focusses on application in several important areas, such as, food, music, fashion and multimedia recommendation. This informative third edition handbook provides a comprehensive, yet concise and convenient reference source to recommender systems for researchers and advanced-level students focused on computer science and data science. Professionals working in data analytics that are using recommendation and personalization techniques will also find this handbook a useful tool. .
    Additional Edition: 9781071621967
    Additional Edition: 9781071621998
    Additional Edition: Erscheint auch als Druck-Ausgabe 9781071621967
    Additional Edition: Erscheint auch als Druck-Ausgabe 9781071621981
    Additional Edition: Erscheint auch als Druck-Ausgabe 9781071621998
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 9
    UID:
    (DE-627)1843964104
    Format: xi, 1060 Seiten , Illustrationen, Diagramme , 24 cm
    Edition: Third edition
    ISBN: 9781071621998 , 9781071621967
    Content: This third edition handbook describes in detail the classical methods as well as extensions and novel approaches that were more recently introduced within this field. It consists of five parts: general recommendation techniques, special recommendation techniques, value and impact of recommender systems, human computer interaction, and applications. The first part presents the most popular and fundamental techniques currently used for building recommender systems, such as collaborative filtering, semantic-based methods, recommender systems based on implicit feedback, neural networks and context-aware methods. The second part of this handbook introduces more advanced recommendation techniques, such as session-based recommender systems, adversarial machine learning for recommender systems, group recommendation techniques, reciprocal recommenders systems, natural language techniques for recommender systems and cross-domain approaches to recommender systems. The third part covers a wide perspective to the evaluation of recommender systems with papers on methods for evaluating recommender systems, their value and impact, the multi-stakeholder perspective of recommender systems, the analysis of the fairness, novelty and diversity in recommender systems. The fourth part contains a few chapters on the human computer dimension of recommender systems, with research on the role of explanation, the user personality and how to effectively support individual and group decision with recommender systems. The last part focusses on application in several important areas, such as, food, music, fashion and multimedia recommendation. This informative third edition handbook provides a comprehensive, yet concise and convenient reference source to recommender systems for researchers and advanced-level students focused on computer science and data science. Professionals working in data analytics that are using recommendation and personalization techniques will also find this handbook a useful tool
    Note: Literaturangaben , Hier auch später erschienene, unveränderte Nachdrucke , Preface.- Introduction.- Part 1: General Recommendation Techniques.- Trust Your Neighbors: A Comprehensive Survey of Neighborhood-based Methods for Recommender Systems (Desrosiers).- Advances in Collaborative Filtering (Koren).- Item Recommendation from Implicit Feedback (Rendle).- Deep Learning for Recommender Systems (Zhang).- Context Aware Re commender Sytems : From Foundatiom to Recent Developments (Bauman).- Semantics and Content-based Recommendations (Musto).- Part 2: Special Recommendation Techniques.- Session-based Recommender Systems (lannoch)..- Adversarial Recommender Systems: Attack,Defense, and Advances (Di Nola).- Group Recommender Systems: Beyond Preferance Aggregation (Masthoff).- People-to-People Reciprocal Recommenders (Koprinska).- Natural Language Processing for Recommender Systems (Sar-Shalom).- Design and Evaluation of Cross-domain Recommender Systems (Cremonesi).- Part 3: Value and Impact of Recommender Systems.- Value and Impact of Recommender Systems (Zanker).- Evaluating Recommender Systems (Shani).- Novelty and Diversity in Recommender Systems (Castells).- Multistakeholder Recommender Systems (Burke).- Fairness in Recommender Systems (Ekstrand).- Part 4: Human Computer Interaction.- Beyond Explaining Single Item Recommendations (Tintarev).- Personality and Recommender Systems (Tkalcic).- Individual and Group Decision Making and Recommender Systems (Jameson).- Part 5: Recommender Systems Applications.- Social Recommender Systems (Guy).- Food Recommender Systems (Trattner).- Music Recommendation Systems: Techniques, Use Cases, and Challenges (Schedl).- Multimedia Recommender Systems: Algorithms and Challenges (Deldjoo).- Fashion Recommender Systems (Dokoohaki).
    Additional Edition: 9781071621974
    Language: English
    URL: Cover  (lizenzpflichtig)
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  • 10
    UID:
    (DE-605)HT021350936
    Format: 1 Online-Ressource (XVII, 1003 p. 144 illus., 78 illus. in color)
    Edition: 3nd ed. 2022
    ISBN: 9781071621974
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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