Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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Journal of the ACM (JACM)
Developing and Validating Trust Measures for e-Commerce: An Integrative Typology
Information Systems Research
ACM Transactions on Information Systems (TOIS)
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Management Science
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
PeerTrust: Supporting Reputation-Based Trust for Peer-to-Peer Electronic Communities
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 10th international conference on Intelligent user interfaces
An ontology of trust: formal semantics and transitivity
ICEC '06 Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet
Inferring binary trust relationships in Web-based social networks
ACM Transactions on Internet Technology (TOIT)
A survey of trust and reputation systems for online service provision
Decision Support Systems
Trust-based recommendation systems: an axiomatic approach
Proceedings of the 17th international conference on World Wide Web
Bayesian reputation modeling in E-marketplaces sensitive to subjecthity, deception and change
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Trust relationship prediction using online product review data
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Using probabilistic confidence models for trust inference in Web-based social networks
ACM Transactions on Internet Technology (TOIT)
A probabilistic model for trust and reputation
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
REQUEST: A Query Language for Customizing Recommendations
Information Systems Research
Bayesian network trust model in peer-to-peer networks
AP2PC'03 Proceedings of the Second international conference on Agents and Peer-to-Peer Computing
ACM Transactions on Management Information Systems (TMIS)
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Trust has emerged as a major impediment to the success of electronic markets and communities where interaction with the strangers is the norm. Social Networks and Online Communities enable interaction with complete strangers, and open up new commercial, political, and social possibilities. But those promises are rarely achieved because it is difficult to trust the online contacts. A common approach to remedy this problem is to compute trust values for the new contacts from the existing trust values in the network. There are two main methods: aggregation and transitivity. Yet, neither method provides satisfactory results because trust networks are sparse and transitivity may not hold. This article develops a Bayesian formulation of the problem, where trust is defined as a conditional probability, and a Bayesian Network analysis is employed to compute the unknown trust values in terms of the known trust values. The algorithms used to propagate conditional probabilities through the network are theoretically sound and based on a long-standing literature on probability propagation in Bayesian networks. Moreover, the context information that is typically ignored in trust literature is included here as a major factor in computing new trust values. These changes have led to significant improvements over existing approaches in the accuracy of computed trust, and with some modifications to the algorithm, in its reach. Real data acquired from Advogato network is used to do extensive testing, and the results confirm the practical value of a theoretically sound Bayesian approach.