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.