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.
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