Computational Trust Models and Machine Learning
1st Edition
Published on 29. October 2014
Book
Hardback
232 pages
978-1-4822-2666-9 (ISBN)
Description
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.
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.
More details
Series
Language
English
Place of publication
Oxford
United States
Publishing group
Taylor & Francis Inc
Target group
Professional and scholarly
Graduate students and researchers of machine learning and data mining techniques.
Illustrations
54 s/w Abbildungen, 15 s/w Tabellen
15 Tables, black and white; 54 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
Weight
468 gr
ISBN-13
978-1-4822-2666-9 (9781482226669)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Xin Liu | Anwitaman Datta | Ee-Peng Lim
Computational Trust Models and Machine Learning
Book
12/2020
1st Edition
Chapman & Hall/CRC
€59.41
Shipment within 15-20 days
Xin Liu | Anwitaman Datta | Ee-Peng Lim
Computational Trust Models and Machine Learning
E-Book
10/2014
1st Edition
Chapman & Hall/CRC
€62.99
Available for download
Xin Liu | Anwitaman Datta | Ee-Peng Lim
Computational Trust Models and Machine Learning
E-Book
10/2014
1st Edition
Chapman and Hall
€63.49
Available for download
Persons
Xin Liu is currently a postdoctoral researcher in the Laboratoire de Systemes d'Informations Repartis, led by Professor Karl Aberer, at Ecole Polytechnique Federale 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.
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.
Content
Introduction. Trust in Online Communities. Judging the Veracity of Claims and Reliability of Sources with Fact-Finders. Web Credibility Assessment. Trust-Aware Recommender Systems. Biases in Trust-Based Systems.