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Idioma: Inglés
Publicado por Taylor and Francis Ltd, GB, 2024
ISBN 10: 1032333227 ISBN 13: 9781032333229
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Añadir al carritoPaperback. Condición: New. Recommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data. Features of this book:Identifies and describes recommender systems for practical usesDescribes how to design, train, and evaluate a recommendation algorithmExplains migration from a recommendation model to a live system with usersDescribes utilization of the data collected from a recommender system to understand the user preferencesAddresses the security aspects and ways to deal with possible attacks to build a robust systemThis book is aimed at researchers and graduate students in computer science, electronics and communication engineering, mathematical science, and data science.
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Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoPaperback. Condición: Brand New. 278 pages. 9.18x6.12x9.21 inches. In Stock.
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Añadir al carritoCondición: New. Monideepa Roy, Pushpendu Kar, Sujoy DattaRecommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their com.
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Añadir al carritoTaschenbuch. Condición: Neu. Recommender Systems | A Multi-Disciplinary Approach | Monideepa Roy (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2024 | CRC Press | EAN 9781032333229 | Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu.
Idioma: Inglés
Publicado por Taylor and Francis Ltd, GB, 2024
ISBN 10: 1032333227 ISBN 13: 9781032333229
Librería: Rarewaves.com UK, London, Reino Unido
EUR 67,65
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Añadir al carritoPaperback. Condición: New. Recommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data. Features of this book:Identifies and describes recommender systems for practical usesDescribes how to design, train, and evaluate a recommendation algorithmExplains migration from a recommendation model to a live system with usersDescribes utilization of the data collected from a recommender system to understand the user preferencesAddresses the security aspects and ways to deal with possible attacks to build a robust systemThis book is aimed at researchers and graduate students in computer science, electronics and communication engineering, mathematical science, and data science.
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 58,30
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Recommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data. Features of this book:Identifies and describes recommender systems for practical usesDescribes how to design, train, and evaluate a recommendation algorithmExplains migration from a recommendation model to a live system with usersDescribes utilization of the data collected from a recommender system to understand the user preferencesAddresses the security aspects and ways to deal with possible attacks to build a robust systemThis book is aimed at researchers and graduate students in computer science, electronics and communication engineering, mathematical science, and data science. 280 pp. Englisch.
Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoPaperback. Condición: Brand New. 278 pages. 9.18x6.12x9.21 inches. In Stock. This item is printed on demand.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Recommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data. Features of this book:Identifies and describes recommender systems for practical usesDescribes how to design, train, and evaluate a recommendation algorithmExplains migration from a recommendation model to a live system with usersDescribes utilization of the data collected from a recommender system to understand the user preferencesAddresses the security aspects and ways to deal with possible attacks to build a robust systemThis book is aimed at researchers and graduate students in computer science, electronics and communication engineering, mathematical science, and data science.