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Computational trust models and machine learning / editors Xin Liu; Anwitaman Datta and Ee-Peng Lim,

Contributor(s): Liu, Xin | Datta, Anwitaman | Lim, Ee-Peng | (Mathematician).
Material type: materialTypeLabelBookSeries: Chapman & Hall/CRC machine learning & pattern recognition series.Publisher: Boca Raton, FL : CRC Press, [2015] ©2015Description: xxiv, 208 pages : illustrations, charts ; 24 cm.ISBN: 9781482226669 (hardback).Subject(s): Computational intelligence | Machine learning | Truthfulness and falsehood | COMPUTERS / Machine Theory | COMPUTERS / Software Development & Engineering / Systems Analysis & Design | TECHNOLOGY & ENGINEERING / Electronics / General | -- Mathematical models | | | DDC classification: 006.31 Other classification: COM037000 | COM051240 | TEC008000
Contents:
1. Introduction -- 2. Trust in online communities -- 3. Judging the veracity of claims and reliability of sources -- 4. Web credibility assessment -- 5. Trust-aware recommender systems -- 6. Biases in trust-based systems.
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 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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Non-fiction 006.31 LIU (Browse shelf) Available 36779

1. Introduction --
2. Trust in online communities --
3. Judging the veracity of claims and reliability of sources --
4. Web credibility assessment --
5. Trust-aware recommender systems --
6. Biases in trust-based systems.

"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"--

Includes bibliographical references (pages 175-201) and index.

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