Computational Trust Models and Machine Learning

Xin Liu

ISBN 10: 1482226669 / ISBN 13: 9781482226669

Edité par Taylor & Francis Inc Okt 2014, 2014

A propos de cet article

Neuware - 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.

N° de réf. du vendeur 9781482226669

Détails bibliographiques

• Titre: Computational Trust Models and Machine Learning
• Éditeur: Taylor & Francis Inc Okt 2014
• Date de parution: 2014
• Langue: anglais
• ISBN à 10 chiffres: 1482226669
• ISBN à 13 chiffres: 9781482226669
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 241 L x 158 H x 18 P (en millimètres) 241 millimètres de largeur par 158 millimètres de hauteur par 18 millimètres de profondeur
• Poids: 470 grammes

A propos de ce titre

Synopsis

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.

À propos de l?auteur

Xin Liu is currently a postdoctoral researcher in the Laboratoire de Systèmes d'Informations Répartis, led by Professor Karl Aberer, at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. Before joining EPFL, Xin received his Ph.D in computer science from Nanyang Technological University in Singapore, supervised by Associate Professor Anwitaman Datta. His current research interests include recommender systems, trust and reputation systems, social computing, and distributed computing. His papers have been accepted at several prestigious academic events, and he has been a program committee member and reviewer for numerous international conferences and journals.

Anwitaman Datta is an associate professor at Nanyang Technological University, Singapore, where he leads the Self-* Aspects of Networked and Distributed Systems Research Group and teaches courses on security management and cryptography and network security. Well published, he has focused his research on P2P storage, decentralized online social networks, structured overlays, and computational trust. His current research interests include the design of resilient large-scale distributed systems, coding for storage, security and privacy, and social media analysis. His projects have been funded by the Singapore Ministry of Education, HP Labs Innovation Research Award, and more.

Ee-Peng Lim is a professor at Singapore Management University (SMU), co-director of the SMU/Carnegie Mellon University Living Analytics Research Center, and associate editor of numerous journals and publications. He holds a Ph.D from the University of Minnesota, Minneapolis, USA and a B.Sc from the National University of Singapore. His current research interests include social network and web mining, information integration, and digital libraries. A former ACM Publications Board member, he currently serves on the steering committees of the International Conference on Asian Digital Libraries, Pacific Asia Conference on Knowledge Discovery and Data Mining, and International Conference on Social Informatics.