Countering feedback sparsity and manipulation in reputation systems

Reputation systems provide a promising way for building trust through social control in collaborative communities by harnessing the community knowledge 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 two closely related problems - feedback sparsity and potential feedback manipulations - and propose a feedback similarity based inference framework. We perform extensive evaluations of various algorithmic components of the framework and evaluate their effectiveness on countering feedback sparsity in the presence of feedback manipulations.