  This paper proposes an approach to detect information relevance during
decision-making from eye movements in order to enable user interface
adaptation. This is a challenging task because gaze behavior varies greatly
across individual users and tasks and groundtruth data is difficult to obtain.
Thus, prior work has mostly focused on simpler target-search tasks or on
establishing general interest, where gaze behavior is less complex. From the
literature, we identify six metrics that capture different aspects of the gaze
behavior during decision-making and combine them in a voting scheme. We
empirically show, that this accounts for the large variations in gaze behavior
and out-performs standalone metrics. Importantly, it offers an intuitive way to
control the amount of detected information, which is crucial for different UI
adaptation schemes to succeed. We show the applicability of our approach by
developing a room-search application that changes the visual saliency of
content detected as relevant. In an empirical study, we show that it detects up
to 97% of relevant elements with respect to user self-reporting, which allows
us to meaningfully adapt the interface, as confirmed by participants. Our
approach is fast, does not need any explicit user input and can be applied
independent of task and user.
