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Full 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.
Contents
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.
