Filtering Out Unfair Ratings in Bayesian Reputation Systems

621 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A survey of trust and reputation systems for online service provision (2007, methodology): “previous) reputation score with the new rating [31,51,49,50,68].”
TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources (2006, background): “[18] extend the BRS and show how it can be used to filter unfair ratings, either unfairly positive or negative, towards a certain agent.”
Engaging rational discrimination: exploring reasons for placing regulatory constraints on decision support systems (2010, unknown): “96 Whitby et al. (2004).”
QoS-Based Service Selection and Ranking with Trust and Reputation Management (2005, methodology): “Other ideas of the trust and reputation management method are based on [19, 20, 21].”
A vision for technology-mediated support for public participation & assistance in mass emergencies & disasters (2010, methodology): “…nature of this system, it is possible to pool perspectives of several users and extract relevant information about the disaster in part by using ratings to rank information accuracy with Bayesian filtering to determine reputation from those ratings (Mui & Mohtashemi, 2002; Whitby et al., 2004).”
Trust network analysis with subjective logic (2006, methodology): “Our model is for example directly compatible with Bayesian reputation systems [12, 25 ], so that reputation scores can be directly imported as trust measures.”