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Computational Trust Models and Machine Learning

Computational Trust Models and Machine Learning

Computational Trust Models and Machine Learning
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Computational Trust Models and Machine Learning Hardback - 2014

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Details

  • Title Computational Trust Models and Machine Learning
  • Author ,
  • Binding Hardback
  • Condition New
  • Pages 232
  • Volumes 1
  • Language ENG
  • Publisher CRC Press
  • Publication date 2014
  • Illustrated Yes
  • Features Bibliography, Glossary, Illustrated, Index
  • Bookseller's Inventory # 21581137
  • ISBN 9781482226669 / 1482226669
  • Weight 1.05 lbs (0.48 kg)
  • Dimensions 9.2 x 6.2 x 0.8 in (23.37 x 15.75 x 2.03 cm)
  • Themes
    • Aspects (Academic): Science/Technology Aspects
  • Category Computers - General Information
  • Library of Congress subjects Machine learning, Computational intelligence
  • Library of Congress Catalogue Number 2014028238
  • Dewey Decimal Code 006.31
  • Quantity available 1

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From the publisher

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.

About the author

Xin Liu is currently a postdoctoral researcher in the Laboratoire de Systmes d'Informations Rpartis, led by Professor Karl Aberer, at cole Polytechnique Fdrale 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.

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