Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
Review on Computational Trust and Reputation Models
Artificial Intelligence Review
Using the h-index to rank influential information scientistss: Brief Communication
Journal of the American Society for Information Science and Technology
A novel two-stage phased modeling framework for early fraud detection in online auctions
Expert Systems with Applications: An International Journal
TIDS: trust-based intrusion detection system for wireless ad-hoc networks
CISIM'12 Proceedings of the 11th IFIP TC 8 international conference on Computer Information Systems and Industrial Management
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Many computational trust models measure collective trust or reputation by the total number or the percentage of (pure) positive feedback a collaborator received from a community. Such measurement usually failed to reflect on how deep a trust is (i.e., how many repeated/re-tested trustworthy collaborations a collaborator has had with another) and also how widely a trust is distributed across a community (i.e. how many trustworthy collaboration occurred with different collaborators). In this work, we have defined Trust Depth (TD), Trust Breadth (TB) to measure these two aspects and proposed a C-Index to measure them in one number. Scenarios are provided to show how the C-index might be used for identifying and selecting collaborative partners.