Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior

By Chrysanthos Dellarocas. ACM Conference on Economics and Computation, 2000

759 citations recorded by the source corpus.

reporting systems have emerged as an important risk management mechanism in online trading communities. However, the predictive value of these systems can be compromised in situations where conspiring buyers intentionally give unfair ratings to sellers or, where sellers discriminate on the quality of service they provide to different buyers. This paper proposes and evaluates a set of mechanisms, which eliminate, or significantly reduce the negative effects of such fraudulent behavior. The proposed mechanisms can be easily integrated into existing online reputation systems in order to safeguard their reliability in the presence of potentially deceitful buyers and sellers.

Subfields: Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/2efd3f1cfc20fc17771612630dc92582ae5afe53

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Citations in the corpus

An Evaluation of Neighbourhood Formation on the Performance of Collaborative Filtering (2004, background): “The reputation system described in (Dellarocas, 2000) is as follows.”
A reputation-based approach for choosing reliable resources in peer-to-peer networks (2002, background): “the peers’ behavior in direct interactions, these systems can keep track also of the peers’ reliability as witnesses, thus allowing for properly weighting their judgment [8, 9].”
Shilling attacks against recommender systems: a comprehensive survey (2012, background): “Although shilling attack or profile injection attack concept is first introduced by O’Mahony et al. (2002a,b) in 2002, Dellarocas (2000) discusses fraudulent behaviors against reputation reporting systems.”
Trust among strangers in internet transactions: Empirical analysis of eBay' s reputation system (2002, background): “Dellarocas has examined models that might discount the effects of a few ratings that were entered in a deliberate effort to manipulate reputations ( Dellarocas 2000 ).”
Is seeing believing?: how recommender system interfaces affect users' opinions (2003, background): “Dellarocas outlines several possible attacks against recommender systems along with a strategy for minimizing the impact of these attacks [4].”
The Different Effects of Online Consumer Reviews on Consumers' Purchase Intentions Depending on Trust in Online Shopping Mall: An Advertising Perspective (2011, background): “Moreover, they can use fake online identities to post dishonest feedback; thus, attempting to improve their reputation or tarnish that of their competitors (Dellarocas, 2000).”
A survey of trust and reputation systems for online service provision (2007, neutral): “Dellarocas (2000) [14] and Withby et al.”
PeerTrust: supporting reputation-based trust for peer-to-peer electronic communities (2004, background): “Dellarocas [10] proposedmechanisms to combat two types of cheating behavior when submitting feedback.”
Shilling recommender systems for fun and profit (2004, methodology): “One way in which the ACF algorithms we test are different from those used in the past [5, 14] is that we do not use negative correlations between items.”
Notions of reputation in multi-agents systems: a review (2002, background): “As shown by Dellarocas (2000), several easy attacks on reputation systems can be staged.”
Reputation-based framework for high integrity sensor networks (2004, background): “E-commerce systems such as e-bay and yahoo auctions have been specifically found to be highly vulnerable to bad-mouthing attacks, in which sellers can collude with buyers to drive other sellers out of the market [Dellarocas 2000].”
Uncheatable Reputation for Distributed Computation Markets (2006, background): “Of concern here are scenarios of ballot stuffing and bad-mouthing [7] in which participants collude in order to build fake pasts.”
A Robust Reputation System for P2P and Mobile Ad-hoc Networks (2004, background): “False accusations are not an issue in positive reputation systems, since no negative information is kept [16, 7 ], however, the disseminated information could still be false praise and result in a good reputation for misbehaving nodes.”
The Effect of Rumor Spreading in Reputation Systems for Mobile Ad-hoc Networks (2003, background): “Slander is not an issue in positive reputation systems, since no negative information is kept [10, 7].”
Computational models of trust and reputation: agents, evolutionary games, and social networks (2002, methodology): “As briefly discussed earlier, several easy attacks on reputation systems can be staged (Dellarocas, 2000). Besides electronic markets, trust and reputation play important roles in distributed systems in general. For example, a trust model features prominently in Zimmermann’s Pretty Good Privacy syst”
A computational model of trust and reputation (2002, background): “As shown by Dellarocas (2000), several easy attacks on reputation systems can be staged.”
Filtering Out Unfair Ratings in Bayesian Reputation Systems (2004, methodology): “Proposals in this category include Dellarocas (2000) [4] and Chen & Singh (2001) [2].”
Detecting product review spammers using rating behaviors (2010, neutral): “Detecting unfair or fraudulent ratings has been studied in several works including [5, 17].”