2012
DOI: 10.4067/s0718-18762012000100002
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Addressing Common Vulnerabilities of Reputation Systems for Electronic Commerce

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Cited by 29 publications
(33 citation statements)
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“…By valuing local and possibly trusted partners' experiences above random shared experiences, the ecosystem members can limit the gains from collusion between isolated actors; we discuss a selection of different approaches to estimating the credibility of reputation information in earlier work [18,23]. Local credibility analysis based on e.g.…”
Section: Addressing Collusion To Generate Positive Experiencesmentioning
confidence: 99%
“…By valuing local and possibly trusted partners' experiences above random shared experiences, the ecosystem members can limit the gains from collusion between isolated actors; we discuss a selection of different approaches to estimating the credibility of reputation information in earlier work [18,23]. Local credibility analysis based on e.g.…”
Section: Addressing Collusion To Generate Positive Experiencesmentioning
confidence: 99%
“…To summarise let us consider the following reputation statement: user x rates 4 stars over 5 product y; user x is the source, 5 star is the claim type, 4 star is the claim value, product y is the target. As outlined in [7], [8], [9] existing reputation systems show many deficiencies: i) lack of connection between reputation statements and its context, e.g judgement about product, delivery, price, interaction with seller is melted in a 5 star claim type plus a detailed written feedback; ii) incomplete or noncomprehensive provided information which causes incorrect perception of the service reputation by the user; iii) no distinction between expressions of fact and opinion, that is between objective and subjective claims; iv) lack of a proper identification mechanism that should allow only effective users of a service to evaluate it thus avoiding fake feedbacks. There are lots of reputation websites that do not check ratings attendability (TripAdvisor.com, RealSelf.com, Glassdoor.com, Honestly.com, RateMyProfessors.com).…”
Section: Schooladvise Design Challenges: a Reputation System For mentioning
confidence: 99%
“…Thereto, we clarify the objectives of the identified classes (functions) and name common examples. Our analysis and selection of reputation systems is based on different surveys [8,13,6,2,1]. Figure 3.2 gives an overview of the primary and secondary classes identified.…”
Section: Hierarchical Component Taxonomymentioning
confidence: 99%
“…Thereby, a wide range of metrics and computation methods for reputation-based trust has been proposed. While most common systems have been introduced in eCommerce, such as eBay's reputation system 1 that allows to rate sellers and buyers, considerable research has also been done in the context of peer-to-peer networks, mobile ad hoc networks, social networks or ensuring data accuracy, relevance and quality in several environments [1]. Computation methods applied range from simple arithmetic over statistical approaches up to graph-based models involving multiple factors such as context information, propagation or personal preferences.…”
Section: Introductionmentioning
confidence: 99%