Recommender System with Machine Learning and Artificial Intelligence /

This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. It compre...

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Detalles Bibliográficos
Autores principales: Mohanty, Sachi (Autor), Chatterjee, Jyotir (Autor), Jain, Sarika (Autor, Editor ), Elngar, Ahmed (Autor), Gupta, Priya (Autor)
Autor Corporativo: Safari, an O'Reilly Media Company
Otros Autores: Gupta, Priya (Professor of computer science) (Editor ), Elngar, Ahmed A. (Editor ), Chatterjee, Jyotir Moy (Editor ), Mohanty, Sachi Nandan (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [Place of publication not identified] : Wiley-Scrivener, 2020.
Edición:1st edition.
Colección:Machine learning in biomedical science and healthcare informatics
Temas:
Recommender systems (Information filtering)
Artificial intelligence.
Machine learning.
Systèmes de recommandation (Filtrage d'information)
Intelligence artificielle.
Apprentissage automatique.
artificial intelligence.
Artificial intelligence
Machine learning
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Descripción
Sumario:This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. It comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior. Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare 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. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural and healthcare sectors.
Descripción Física:1 online resource (448 pages).
ISBN:9781119711575
1119711576

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