Implicit Adaptation of User Preferences in Pervasive Systems

S. McBurney, E. Papadopoulou, N. Taylor, M. H. Williams
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引用次数: 5

Abstract

User preferences have an essential role to play in decision making in pervasive systems. However, building up and maintaining a set of user preferences for an individual user is a nontrivial exercise. Relying on the user to input preferences has been found not to work and the use of different forms of machine learning are being investigated. This paper is concerned with the problem of updating a set of preferences when a new aspect of an existing preference is discovered. A basic algorithm (with variants) is given for handling this situation. This has been developed for the Daidalos and Persist pervasive systems. Some research issues are also discussed.
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普适系统中用户偏好的隐式适应
用户偏好在普适系统的决策中起着至关重要的作用。但是,为单个用户建立和维护一组用户首选项是一项非常重要的工作。人们发现,依靠用户输入偏好是行不通的,目前正在研究使用不同形式的机器学习。本文研究了当现有偏好的新方面被发现时,如何更新一组偏好。给出了处理这种情况的基本算法(带有变量)。这是为Daidalos和Persist普及系统开发的。本文还讨论了一些研究问题。
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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