Toward Experiential Utility Elicitation for Interface Customization
This work addresses the challenge of accurately modeling user preferences for automated assistance in software domains, offering a more realistic method for interface customization that could enhance user experience, though it appears incremental in its approach to utility elicitation.
The paper tackled the problem of eliciting user preferences for interface customization by comparing experiential utility elicitation methods, which use realistic scenarios, to predictive methods based on hypothetical scenarios. The results showed that the experiential approach helps users understand stochastic outcomes and better appreciate sequential utility, indicating improved accuracy and efficiency in learning preferences.
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.