Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems 2007
DOI: 10.1145/1329125.1329227
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Incentive compatible ranking systems

Abstract: Ranking systems are a fundamental ingredient of multi-agent environments and Internet Technologies. These settings can be viewed as social choice settings with two distinguished properties: the set of agents and the set of alternatives coincide, and the agents' preferences are dichotomous, and therefore classical impossibility results do not apply. In this paper we initiate the study of incentives in ranking systems, where agents act in order to maximize their position in the ranking, rather than to obtain a c… Show more

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Cited by 17 publications
(21 citation statements)
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“…Google's search is a special kind of rating system (in which one user's Web links to the Web pages of another are the (indirectly) contributed ratings). It collects votes in a binary format which are susceptible to manipulations Altman and Tennenholtz [2005], Cheng and Friedman [2005]. The same is true for social tagging systems, e.g., Del.icio.us [Ramezani et al, 2008].…”
Section: Numeric Manipulationsmentioning
confidence: 99%
“…Google's search is a special kind of rating system (in which one user's Web links to the Web pages of another are the (indirectly) contributed ratings). It collects votes in a binary format which are susceptible to manipulations Altman and Tennenholtz [2005], Cheng and Friedman [2005]. The same is true for social tagging systems, e.g., Del.icio.us [Ramezani et al, 2008].…”
Section: Numeric Manipulationsmentioning
confidence: 99%
“…Utility Structure. We shall now define incentive compatibility, following Altman and Tennenholtz [2007]. We require that a ranking system will not rank agents better when they apply a manipulation, but we assume that the agents are interested only in their own ranking (and not, say, in the ranking of those they prefer).…”
Section: Ranked Independence Of Irrelevant Alternativesmentioning
confidence: 99%
“…The adaptive classification of users and contents will allow the system to recommend the most suitable contents for a particular user based on his adaptive profile. Traditional recommendation algorithms can not cope with this objective [1,4,6,7,9,10,13]. …”
Section: The Proposed Content-zapping Servicementioning
confidence: 99%