Home Computational Trust Models and Machine Learning by Xin Liu (Editor); Anwitaman Datta (Editor); Ee-Peng Lim (Editor) (ISBN: 9780367739331)

Stock photo: cover may vary

Computational Trust Models and Machine Learning Paperback -

Name: Computational Trust Models and Machine Learning
Price: 49.04 GBP
Availability: InStock
Author: Xin Liu
ISBN: 9780367739331

by Xin Liu (Editor); Anwitaman Datta (Editor); Ee-Peng Lim (Editor)

Add to wish list

Browse more copies for sale ↓

Reader reviews for Computational Trust Models and Machine Learning

Write a review for this book

Important Terms and Guidelines

Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
Reviews must be at least 140 characters in length.
Please do not reveal critical plot elements.
This is not a help line. Contact customer support if you need help.

Your review must not include:

Obscenities, discriminatory language, or other insulting language not suitable for public domain
Advertisements, “spam” content, or references to other products, offers or websites.
Email addresses, URLs, phone numbers, physical addresses or other contact information.
Overly critical comments about other reviews or reviewers
Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
Availability, price, or alternative ordering/shipping information

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.

Details

Title Computational Trust Models and Machine Learning
Author Xin Liu (Editor); Anwitaman Datta (Editor); Ee-Peng Lim (Editor)
Binding Paperback
Pages 232
Volumes 1
Language ENG
Publisher CRC Press
ISBN 9780367739331 / 036773933X
Weight 0.75 lbs (0.34 kg)
Dimensions 9.2 x 6.1 x 0.6 in (23.37 x 15.49 x 1.52 cm)
Themes
- Aspects (Academic): Science/Technology Aspects
Category Computers - General Information
Dewey Decimal Code 006.31

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.

More Copies for Sale