Recommender System with Machine Learning and Artificial Intelligence

• Verfasst von: Mohanty, Sachi [VerfasserIn]
• Verfasst von: Chatterjee, Jyotir [VerfasserIn]
• Verfasst von: Jain, Sarika [VerfasserIn]
• Verfasst von: Elngar, Ahmed [VerfasserIn]
• Verfasst von: Gupta, Priya [VerfasserIn]
• Titel: Recommender System with Machine Learning and Artificial Intelligence
• Institutionen: Safari, an O’Reilly Media Company. [MitwirkendeR]
• Verf.angabe: Mohanty, Sachi
• Ausgabe: 1st edition
• Verlagsort: [Erscheinungsort nicht ermittelbar]
• Verlag: Wiley-Scrivener
• Jahr: 2020
• Umfang: 1 online resource (448 pages)
• Fussnoten: Online resource; Title from title page (viewed July 8, 2020)
• ISBN: 978-1-119-71157-5
• ComputerInfo: Mode of access: World Wide Web.
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Electronic books ; local
• K10plus-PPN: 1739146468
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m3806975345
• Lokale URL Inst.: Zum Volltext

Abstract

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 & healthcare sectors.