LAUSR

We focus on enhancing the user experience by predicting entries when a form is filled, according to past interactions. The purpose of having a predictive model of form filling is to reduce the amount of time required to fill a form, and thus to reduce the fatigue and repetitiveness associated to this common task. Generally predictive models ignore the values entered by users in the other fields in the form, and just focus on the value getting entered at the current field. This is a limit to the model capabilities. Instead, we are aimed at predicting the sequence of entries in a form, instead of the value of single fields in isolation. This is done by means of inference over a Bayesian network, able to compute the a posteriori probability that remaining fields will assume certain values, given the set of values entered so far. The model structure and parameters can be learned from a dataset of past entries. The paper investigates computational and convergence issues under both the closed world assumption and the open world assumptions. As case study, we considered forms used for online payment of money order used at Poste Italiane, and we exploited this approach to prototype two different solutions for desktop and mobile applications. Results of experimentation with a user test group prove the proposed approach is able to provide an effective and appreciated support in filling a form.