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  • 1
    Online Resource
    Online Resource
    New York : Springer
    UID:
    b3kat_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 ; Aufsatzsammlung
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 2
    Online Resource
    Online Resource
    Cham : Springer International Publishing | Cham : Springer
    UID:
    b3kat_BV049321427
    Format: 1 Online-Ressource (VII, 985 p. 214 illus., 156 illus. in color)
    Edition: 3rd ed. 2023
    ISBN: 9783031246289
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-24627-2
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-24629-6
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-24630-2
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 3
    Online Resource
    Online Resource
    New York, NY ; s.l. : Springer New York | Imprint: Springer
    UID:
    b3kat_BV041743233
    Format: 1 Online-Ressource (Ill.) , X, 88 p.
    Edition: Online-Ausgabe Springer eBook Collection / Computer Science
    ISBN: 9781493905393
    Series Statement: SpringerBriefs in Electrical and Computer Engineering
    Additional Edition: Reproduktion von Dahan, Haim Proactive Data Mining with Decision Trees 2014
    Additional Edition: Erscheint auch als Druckausgabe ISBN 978-1-4939-0538-6
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Informatik ; Data Mining ; Informationssystem ; Entscheidungsbaum
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  • 4
    UID:
    b3kat_BV039980817
    Format: VIII, 92 S. , Ill., graph. Darst.
    ISBN: 9781461420521 , 9781461420538
    Series Statement: SpringerBriefs in computer science
    Note: Lizenzpflichtig
    Language: English
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  • 5
    Online Resource
    Online Resource
    New York, NY : Springer US | New York, NY : Imprint: Springer
    UID:
    gbv_1800270518
    Format: 1 Online-Ressource(XI, 1060 p. 129 illus., 105 illus. in color.)
    Edition: 3rd ed. 2022.
    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 9781071621981
    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
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  • 6
    Book
    Book
    New Jersey [u.a.] : World Scientific
    UID:
    b3kat_BV036107722
    Format: XV, 225 S. , graph. Darst.
    ISBN: 9789814271066 , 9814271063
    Series Statement: Series in machine perception and artificial intelligence 75
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Mustererkennung
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  • 7
    UID:
    b3kat_BV025543269
    Format: ca. 350 S.
    ISBN: 9781441900470
    Language: English
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  • 8
    Online Resource
    Online Resource
    Singapore : World Scientific Publishing Co. Pte. Ltd
    UID:
    gbv_1684654033
    Format: 1 Online-Ressource (300 pages)
    Edition: Second edition
    ISBN: 9789811201967 , 9789811201974
    Series Statement: Series in machine perception and artificial intelligence volume 85
    Content: "This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced. Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized. The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods."--
    Note: Includes bibliographical references and index
    Additional Edition: ISBN 9789811201950
    Additional Edition: Erscheint auch als Druckausgabe Rokach, Lior Ensemble learning New Jersey : World Scientific, 2019 ISBN 9789811201950
    Language: English
    Keywords: Maschinelles Lernen ; Mustererkennung ; Algorithmus ; Electronic books
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  • 9
    Book
    Book
    New York [u.a.] :Springer,
    UID:
    kobvindex_ZIB000014517
    Format: XXIX, 842 S. : , Ill., graph. Darst. ; , 235 mm x 155 mm
    ISBN: 978-0-387-85819-7 , 978-0-387-85819-7
    Note: c 2011
    Language: English
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