Foundations of Context-aware Preference Propagation

P. Ciaccia, D. Martinenghi, Riccardo Torlone
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引用次数: 7

Abstract

Preferences are a fundamental ingredient in a variety of fields, ranging from economics to computer science, for deciding the best choices among possible alternatives. Contexts provide another important aspect to be considered in the selection of the best choices, since, very often, preferences are affected by context. In particular, the problem of preference propagation from more generic to more specific contexts naturally arises. Such a problem has only been addressed in a very limited way and always resorts to practical, ad hoc approaches. To fill this gap, in this article, we analyze preference propagation in a principled way and adopt an abstract context model without making any specific assumptions on how preferences are stated. Our framework only requires that the contexts form a partially ordered set and that preferences define a strict partial order on the objects of interest. We first formalize the basic properties that any propagation process should satisfy. We then introduce an algebraic model for preference propagation that relies on two abstract operators for combining preferences, and, under mild assumptions, we prove that the only possible interpretations for such operators are the well-known Pareto and Prioritized composition. We then study several propagation methods based on such operators and precisely characterize them in terms of the stated properties. We finally identify a method meeting all the requirements, on the basis of which we provide an efficient algorithm for preference propagation.
上下文感知偏好传播的基础
从经济学到计算机科学,偏好是许多领域的基本要素,用于在可能的选择中做出最佳选择。在选择最佳选择时,上下文提供了另一个需要考虑的重要方面,因为偏好经常受到上下文的影响。特别是,从更一般的上下文到更具体的上下文的偏好传播问题自然会出现。这一问题只以非常有限的方式得到解决,而且总是采取实际的、特别的办法。为了填补这一空白,在本文中,我们以一种有原则的方式分析偏好传播,并采用一个抽象的上下文模型,而不对偏好的陈述方式做出任何具体假设。我们的框架只要求上下文形成部分有序的集合,并且首选项在感兴趣的对象上定义严格的部分顺序。我们首先形式化任何传播过程都应满足的基本性质。然后,我们引入了偏好传播的代数模型,该模型依赖于两个抽象算子来组合偏好,并且,在温和的假设下,我们证明了这些算子的唯一可能解释是众所周知的帕累托和优先组合。然后,我们研究了基于这些算子的几种传播方法,并根据所述性质对它们进行了精确的表征。最后确定了一种满足所有要求的方法,并在此基础上给出了有效的偏好传播算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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