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Computational trust models and machine learning

Author: Xin Liu, (Mathematician); Anwitaman Datta; Ee-Peng Lim
Publisher: Boca Raton : CRC Press/Taylor & Francis Group, 2014.
Series: Chapman & Hall/CRC machine learning & pattern recognition series.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
"This book provides an introduction to computational trust models from a machine learning perspective. After reviewing traditional computational trust models, it discusses a new trend of applying formerly unused machine learning methodologies, such as supervised learning. The application of various learning algorithms, such as linear regression, matrix decomposition, and decision trees, illustrates how to translate  Read more...
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Document Type: Book
All Authors / Contributors: Xin Liu, (Mathematician); Anwitaman Datta; Ee-Peng Lim
ISBN: 9781482226669 1482226669
OCLC Number: 884440170
Description: xxiv, 208 pages : illustrations, charts ; 24 cm.
Contents: Trust in online communities --
Judging the veracity of claims and reliability of sources --
Web credibility assessment --
Trust-aware recommender systems --
Biases in trust-based systems.
Series Title: Chapman & Hall/CRC machine learning & pattern recognition series.
Responsibility: editors Xin Liu, EPFL, Lausanne, Switzerland, Anwitaman Datta, Nanyang Technological University, Singapore, Ee-Peng Lim, Singapore Management University.

Abstract:

"This book provides an introduction to computational trust models from a machine learning perspective. After reviewing traditional computational trust models, it discusses a new trend of applying formerly unused machine learning methodologies, such as supervised learning. The application of various learning algorithms, such as linear regression, matrix decomposition, and decision trees, illustrates how to translate the trust modeling problem into a (supervised) learning problem. The book also shows how novel machine learning techniques can improve the accuracy of trust assessment compared to traditional approaches"--

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