Visualizing Unfair Ratings in Online Reputation Systems

Reputation systems provide a valuable method to measure the trustworthiness of sellers or the quality of products in an e-commerce environment. Due to their economic importance, reputation systems are subject to many attacks. A common problem are unfair ratings which are used to unfairly increase or decrease the reputation of an entity. Although being of high practical relevance, unfair rating attacks have only rarely been considered in literature. The few approaches that have been proposed are furthermore quite non-transparent to the user. In this work, we employ visual analytics to identify colluding digital identities. The ultimate benefit of our approach is the transparent revelation of the true reputation of an entity by interactively using both endogenous and exogenous discounting methods. We thereto introduce a generic conceptual design of a visual analytics component that is independent of the underlying reputation system. We then describe how this concept was implemented in a software prototype. Subsequently, we demonstrate its proper functioning by means of an empirical study based on two real-world datasets from eBay and Epinions. Overall, we show that our approach notably enhances transparency, bares an enormous potential and might thus lead to substantially more robust reputation systems and enhanced user experience.