Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An Introduction to Variational Methods for Graphical Models
Machine Learning
TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources
Autonomous Agents and Multi-Agent Systems
What Trust Means in E-Commerce Customer Relationships: An Interdisciplinary Conceptual Typology
International Journal of Electronic Commerce
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 network inference for online rating data using generative models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-layer cognitive filtering by behavioral modeling
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
iCLUB: an integrated clustering-based approach to improve the robustness of reputation systems
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
SARC: subjectivity alignment for reputation computation
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Modeling contextual agreement in preferences
Proceedings of the 23rd international conference on World wide web
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It is indispensable for users to evaluate the trustworthiness of other users (referred to as advisors), to cope with possible misleading opinions provided by them. Advisors' misleading opinions may be induced by their dishonesty, subjectivity difference with users, or both. Existing approaches do not well distinguish the two different causes. In this paper, we propose a novel probabilistic graphical trust model to separately consider these two factors, involving three types of latent variables: benevolence, integrity and competence of advisors, trust propensity of users, and subjectivity difference between users and advisors. Experimental results on real datasets demonstrate that our method advances state-of-the-art approaches to a large extent.