Anwitaman Datta – författare

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 communitiesDescribes how machine learning techniques are employed to build robust reputation systemsExplores two distinctive approaches to determining credibility of resources—one where the human role is implicit, and one that leverages human input explicitlyShows how decision support can be facilitated by computational trust modelsDiscusses collaborative filtering-based trust aware recommendation systemsDefines a framework for translating a trust modeling problem into a learning problemInvestigates the objectivity of human feedback, emphasizing the need to filter out outlying opinionsComputational Trust Models and Machine Learning effectively demonstrates how novel machine learning techniques can improve the accuracy of trust assessment.

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

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 communitiesDescribes how machine learning techniques are employed to build robust reputation systemsExplores two distinctive approaches to determining credibility of resources—one where the human role is implicit, and one that leverages human input explicitlyShows how decision support can be facilitated by computational trust modelsDiscusses collaborative filtering-based trust aware recommendation systemsDefines a framework for translating a trust modeling problem into a learning problemInvestigates the objectivity of human feedback, emphasizing the need to filter out outlying opinionsComputational Trust Models and Machine Learning effectively demonstrates how novel machine learning techniques can improve the accuracy of trust assessment.

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

This book constitutes the proceedings of the Third International Conference on Social Informatics, SocInfo 2011, held in Singapore in October 2011. The 15 full papers, 8 short papers and 13 posters included in this volume were carefully reviewed and selected from 68 full paper and 13 poster submissions. The papers are organized in topical sections named: network analysis; eGovernance and knowledge management; applications of network analysis; community dynamics; case studies; trust, privacy and security; peer-production.