Computational Trust Models and Machine Learning

• Verfasst von: Liu, Xin [VerfasserIn]
• Verfasst von: Datta, Anwitaman [VerfasserIn]
• Verfasst von: Lim, Ee-Peng [VerfasserIn]
• Titel: Computational Trust Models and Machine Learning
• Institutionen: Safari, an O’Reilly Media Company.
• Verf.angabe: Liu, Xin
• Ausgabe: 1st edition
• Verlagsort: [Erscheinungsort nicht ermittelbar]
• Verlag: Chapman and Hall/CRC
• Jahr: 2014
• Umfang: 1 online resource (232 pages)
• Fussnoten: Online resource; Title from title page (viewed October 29, 2014)
• ComputerInfo: Mode of access: World Wide Web.
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Electronic books ; local
• K10plus-PPN: 1694870448
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m362485402X
• Lokale URL Inst.: Zum Volltext

Abstract

Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including multi-agent systems, online social networks, and communication systems. Identifying trust modeling challenges that cannot be addressed by traditional approaches, this book: Explains how reputation-based systems are used to determine trust in diverse online communities Describes how machine learning techniques are employed to build robust reputation systems Explores two distinctive approaches to determining credibility of resources—one where the human role is implicit, and one that leverages human input explicitly Shows how decision support can be facilitated by computational trust models Discusses collaborative filtering-based trust aware recommendation systems Defines a framework for translating a trust modeling problem into a learning problem Investigates the objectivity of human feedback, emphasizing the need to filter out outlying opinions Computational Trust Models and Machine Learning effectively demonstrates how novel machine learning techniques can improve the accuracy of trust assessment.