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Recommender Systems: Algorithms and their Applications [electronic resource] / by Pushpendu Kar, Monideepa Roy, Sujoy Datta.

By: Kar, Pushpendu [author.].
Contributor(s): Roy, Monideepa [author.] | Datta, Sujoy [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Transactions on Computer Systems and Networks: Publisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2024Edition: 1st ed. 2024.Description: XIV, 165 p. 64 illus., 43 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9789819705382.Subject(s): Database management | Artificial intelligence | Quantitative research | Database Management System | Artificial Intelligence | Data Analysis and Big DataAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 005.7 Online resources: Click here to access online
Contents:
Introduction -- Overview of Recommendtion system Algorithms -- Collaborative Filtering -- Matrix decomposition for Recommendtion -- Clustering.
In: Springer Nature eBookSummary: The book includes a thorough examination of the many types of algorithms for recommender systems, as well as a comparative analysis of them. It addresses the problem of dealing with the large amounts of data generated by the recommender system. The book also includes two case studies on recommender system applications in healthcare monitoring and military surveillance. It demonstrates how to create attack-resistant and trust-centric recommender systems for sensitive data applications. This book provides a solid foundation for designing recommender systems for use in healthcare and defense.
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Introduction -- Overview of Recommendtion system Algorithms -- Collaborative Filtering -- Matrix decomposition for Recommendtion -- Clustering.

The book includes a thorough examination of the many types of algorithms for recommender systems, as well as a comparative analysis of them. It addresses the problem of dealing with the large amounts of data generated by the recommender system. The book also includes two case studies on recommender system applications in healthcare monitoring and military surveillance. It demonstrates how to create attack-resistant and trust-centric recommender systems for sensitive data applications. This book provides a solid foundation for designing recommender systems for use in healthcare and defense.

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