Designing reputation mechanisms

Dellarocas, Chrysanthos; Dini, Federico; Spagnolo, Giancarlo

doi:10.1017/cbo9780511492556.019

Cited by 48 publications

(40 citation statements)

References 22 publications

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“…Whitby et al (2005) are among researchers who use the Bayesian rule to study the exclusion of unfair ratings. Dellarocas (2003Dellarocas ( , 2005Dellarocas ( , 2006 use the collaborative filtering technique and study how to exclude unfair ratings and build a reliable rating system. is closely related to the study by Zhang (2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Ensuring Trust Online through the Wisdom of Crowd

Zhang

2014

JIEBS

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Section: Literature Reviewmentioning

confidence: 99%

Ensuring Trust Online through the Wisdom of Crowd

Zhang

2014

JIEBS

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“…In order to obtain anonymity of raters, interactions and ratings related to these interactions need to be unlinkable. This can be reached by a reputation provider who only calculates a new user reputation after it collected not only one but several ratings [16], or who only publishes an estimation of the actual reputation [14]. Further, a rater can be anonymous against the reputation provider by using convertible credentials [10] or electronic cash [15,17] or involve Trusted Third Parties similarly for separating interactions and ratings while preventing attacks [18,19].…”

Section: Centralized Communication Protocols That Allow Users Tomentioning

confidence: 99%

Privacy-Respecting Reputation for Wiki Users

2011

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Wikis are popular tools for creation and sharing of content. Integrated reputation systems allow to assess expertise and reliability of authors and thus to support trust in the wiki content. Yet, results from our empirical study indicate that the disclosure of user reputation evokes privacy issues. As a solution for this conflict between the need to evaluate trustworthiness of users and protecting their privacy, we present a privacy-respecting reputation system for wikis that we realized as OpenSource-Extension for the wiki software MediaWiki.

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“…If P S is halfway between two rating options, then without loss of generality, the rater will choose the higher integer option. 5 Example 1 Suppose two people rate a service using the metric int [1,5]. using a linear transformation.…”

Section: A Rating System Is a Set { Int[mn] R } Where Int[mn] Is Tmentioning

confidence: 99%

“…Raters having a P S [−1,1] less than −.5 will rate −1, those between −.5 and .5 will rate 0, and those with P S [−1,1] larger than .5 will rate 1. In order for the scale and binary metrics to be equivalent, the pre-image of −.5 and .5 under the linear transformation, given by Example 3 Consider the scale metric [1,5]. for simplicity, however, these can be generalized to any two points .…”

Section: Scale and Binary Equivalencymentioning

confidence: 99%

“…In Example 1 and 2 above, person A's P S is represented as a 0 rating in the binary system and a 2 in the scale system. Because the 2 rating is below the neutral rating in the [1,5] scale (i.e., below the midpoint of 3) and 0 is the neutral rating in the binary metric, the binary system actually represents A's P S at a higher value than the scaled. The opinion in the binary system is thus interpreted as higher than A's opinion in the scale.…”

Section: Implications Of Differences Between Binary and Scale Metricmentioning

confidence: 99%

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Decision Making Using Rating Systems: When Scale Meets Binary

Bargagliotti

2009

SSRN Journal

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Rating systems measuring quality of products and services (i.e., the state of the world) are widely used to solve the asymmetric information problem in markets. Decision makers typically make binary decisions such as buy/hold/sell based on aggregated individuals' opinions presented in the form of ratings. Problems arise, however, when different rating metrics and aggregation procedures translate the same underlying popular opinion to different conclusions about the true state of the world. This paper investigates the inconsistency problem by examining the mathematical structure of the metrics and their relationship to the aggregation rules. It is shown that at the individual level, the only scale metric (1,. . . ,N) that reports people's opinion equivalently in the a binary metric (-1, 0, 1) is one where N is odd and N-1 is not divisible by 4. At aggregation level, however, the inconsistencies persist regardless of which scale metric is used. In addition, this paper provides simple tools to determine whether the binary and scale rating systems report the same information at individual level, as well as when the systems differ at the aggregation level.

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Designing reputation mechanisms

Cited by 48 publications

References 22 publications

Ensuring Trust Online through the Wisdom of Crowd

Ensuring Trust Online through the Wisdom of Crowd

Privacy-Respecting Reputation for Wiki Users

Decision Making Using Rating Systems: When Scale Meets Binary

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