Countering Sparsity and Vulnerabilities in Reputation Systems
نویسندگان
چکیده
While web applications provide enormous opportunities, they also present potential threats and risks due to a lack of trust among users. Reputation systems provide a promising way for building trust through social control by harnessing the community feedback in the form of feedback. However, reputation systems also introduce vulnerabilities due to potential manipulations by dishonest or malicious players. In this paper, we focus on important feedback aggregation related vulnerabilities, in particular, feedback sparsity with potential feedback manipulations, and develop resilient models and techniques. We propose similarity measures for differentiating dishonest feedbacks from honest ones and propose an inference framework to address the sparsity issues. We perform extensive evaluations of various algorithmic component of the framework and evaluate their effectiveness in countering feedback sparsity.
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