Inverse Foraging: Inferring Users' Interest in Pervasive Displays

Users' engagement with pervasive displays has been extensively studied, however, determining how their content is interesting remains an open problem. Tracking of body postures and gaze has been explored as an indication of attention; still, existing works have not been able to estimate the interest of passers-by from readily available data, such as the display viewing time. This article presents a simple yet accurate method of estimating users' interest in multiple content items shown at the same time on displays. The proposed approach builds on the information foraging theory, which assumes that users optimally decide on the content they consume. Through inverse foraging, the parameters of a foraging model are fitted to the values of viewing times observed in practice, to yield estimates of user interest. Different foraging models are evaluated by using synthetic data and with a controlled user study. The results demonstrate that inverse foraging accurately estimates interest, achieving an R2 above 70% in comparison to self-reported interest. As a consequence, the proposed solution allows to dynamically adapt the content shown on pervasive displays, based on viewing data that can be easily obtained in field deployments.