面向用户界面定制的体验效用获取
Toward Experiential Utility Elicitation for Interface Customization
摘要：
用户对自动化帮助的偏好通常差异很大，这取决于情况以及帮助的质量或呈现方式。开发有效的模型来在线学习个人偏好需要领域模型，该模型将用户行为的观察与其效用函数相关联，而效用函数又可以使用效用启发技术来构建。然而，大多数启发式方法要求用户的预测效用的基础上假设的情况下，而不是更现实的经验效用。这在界面定制中尤其如此，用户被要求评估新颖的界面设计。我们提出了经验的实用程序的定制方法，并比较这些预测方法。由于经验丰富的实用程序已被认为是更好地反映行为决策的真实偏好，这里的目的是调查准确和有效的程序，适用于软件领域。与传统的启发，我们的研究结果表明，经验的方法可以帮助人们理解随机的结果，以及更好地欣赏智能援助的顺序效用。
英文摘要：
User preferences for automated assistance often vary widely, depending on the situation, and quality or presentation of help. Developing effectivemodels to learn individual preferences online requires domain models that associate observations of user behavior with their utility functions, which in turn can be constructed using utility elicitation techniques. However, most elicitation methods ask for users' predicted utilities based on hypothetical scenarios rather than more realistic experienced utilities. This is especially true in interface customization, where users are asked to assess novel interface designs. We propose experiential utility elicitation methods for customization and compare these to predictivemethods. As experienced utilities have been argued to better reflect true preferences in behavioral decision making, the purpose here is to investigate accurate and efficient procedures that are suitable for software domains. Unlike conventional elicitation, our results indicate that an experiential approach helps people understand stochastic outcomes, as well as better appreciate the sequential utility of intelligent assistance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
