An Axiomatic Approach to Personalized Ranking Systems

被引：11

作者：

Altman, Alon ^{[1
]}

Tennenholtz, Moshe ^{[2
]}

机构：

[1] Stanford Univ, Stanford, CA 94305 USA

[2] Technion Israel Inst Technol, Haifa, Israel

来源：

JOURNAL OF THE ACM | 2010年 / 57卷 / 04期

关键词：

Algorithms; Economics; Theory; Axiomatic approach; manipulation; ranking systems; social networks;

D O I：

10.1145/1734213.1734220

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Personalized ranking systems and trust systems are an essential tool for collaboration in a multi-agent environment. In these systems, trust relations between many agents are aggregated to produce a personalized trust rating of the agents. In this article, we introduce the first extensive axiomatic study of this setting, and explore a wide array of well-known and new personalized ranking systems. We adapt several axioms ( basic criteria) from the literature on global ranking systems to the context of personalized ranking systems, and fully classify the set of systems that satisfy all of these axioms. We further show that all these axioms are necessary for this result.

引用

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