<p>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: </p><p></p><ul> <p><li>Explains how reputation-based systems are used to determine trust in diverse online communities</li> <p><li>Describes how machine learning techniques are employed to build robust reputation systems</li> <p><li>Explores two distinctive approaches to determining credibility of resources--one where the human role is implicit, and one that leverages human input explicitly</li> <p><li>Shows how decision support can be facilitated by computational trust models</li> <p><li>Discusses collaborative filtering-based trust aware recommendation systems</li> <p><li>Defines a framework for translating a trust modeling problem into a learning problem</li> <p><li>Investigates the objectivity of human feedback, emphasizing the need to filter out outlying opinions</li> </ul><p></p><p><b>Computational Trust Models and Machine Learning </b>effectively demonstrates how novel machine learning techniques can improve the accuracy of trust assessment.</p>