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    Recommender Systems: The Textbook

    Recommender Systems by Aggarwal, Charu C.;

    The Textbook

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      • Publisher's listprice EUR 71.68
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        30 406 Ft (28 958 Ft + 5% VAT)
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      • Discounted price 27 973 Ft (26 641 Ft + 5% VAT)

    30 406 Ft

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    Availability

    Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
    Not in stock at Prospero.

    Why don't you give exact delivery time?

    Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.

    Product details:

    • Edition number Softcover reprint of the original 1st ed. 2016
    • Publisher Springer
    • Date of Publication 25 April 2018
    • Number of Volumes 1 pieces, Previously published in hardcover

    • ISBN 9783319806198
    • Binding Paperback
    • No. of pages498 pages
    • Size 254x178 mm
    • Weight 9656 g
    • Language English
    • Illustrations 61 Illustrations, black & white; 18 Illustrations, color
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    Short description:

    This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity.  The chapters of this book  are organized into three categories:

    - Algorithms and evaluation:  These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation.

    - Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored.

    - Advanced topics and applications:  Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed.

    In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications.

    Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.
    About the Author: Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T.J. Watson Research Center in Yorktown Heights, New York. He completed his B.S. from IIT Kanpur in 1993 and his Ph.D. from the Massachusetts Institute of Technology in 1996. He has published more than 300 papers in refereed conferences and journals, and has applied for or been granted more than 80 patents. He is author or editor of 15 books, including a textbook on data mining and a comprehensive book on outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He has received several internal and external awards, including the EDBT Test-of-Time Award (2014) and the IEEE ICDM Research Contributions Award (2015). He has also served as program or general chair of many major conferences in data mining. He is a fellow of the SIAM, ACM, and the IEEE, for ?contributions to knowledge discovery and data mining algorithms.?

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    Long description:

    This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity.  The chapters of this book  are organized into three categories:

    Algorithms and evaluation:  These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation.

    Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data,spatial data, social data, tagging data, and trustworthiness are explored.

    Advanced topics and applications:  Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed.

    In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications.

    Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.

    ?Charu Aggarwal, a well-known, reputable IBM researcher, has taken the time to distill the advances in the design of recommender systems since the advent of the web ? . Extensive bibliographic notes at the end of each chapter and more than 700 references in the book bibliography make this monograph an excellent resource for both practitioners and researchers. ? Without a doubt, this is an excellent addition to my bookshelf!? (Fernando Berzal, Computing Reviews, February, 2017)

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    Table of Contents:

    An Introduction to Recommender Systems.- Neighborhood-Based Collaborative Filtering.- Model-Based Collaborative Filtering.- Content-Based Recommender Systems.- Knowledge-Based Recommender Systems.- Ensemble-Based and Hybrid Recommender Systems.- Evaluating Recommender Systems.- Context-Sensitive Recommender Systems.- Time- and Location-Sensitive Recommender Systems.- Structural Recommendations in Networks.- Social and Trust-Centric Recommender Systems.- Attack-Resistant Recommender Systems.- Advanced Topics in Recommender Systems.

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