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This paper proposes an approach to detect information relevance duringdecision-making from eye movements in order to enable user interfaceadaptation. This is a challenging task because gaze behavior varies greatlyacross individual users and tasks and groundtruth data is difficult to obtain.Thus, prior work has mostly focused on simpler target-search tasks or onestablishing general interest, where gaze behavior is less complex. From theliterature, we identify six metrics that capture different aspects of the gazebehavior during decision-making and combine them in a voting scheme. Weempirically show, that this accounts for the large variations in gaze behaviorand out-performs standalone metrics. Importantly, it offers an intuitive way tocontrol the amount of detected information, which is crucial for different UIadaptation schemes to succeed. We show the applicability of our approach bydeveloping a room-search application that changes the visual saliency ofcontent detected as relevant. In an empirical study, we show that it detects upto 97% of relevant elements with respect to user self-reporting, which allowsus to meaningfully adapt the interface, as confirmed by participants. Ourapproach is fast, does not need any explicit user input and can be appliedindependent of task and user.

Detecting Relevance during Decision-Making from Eye Movements for UI Adaptation