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The existing approaches to identify personalized salience zones of a Web pagedo not consider the dynamic behavior in time of the Web user's gaze or thealterations of its content. For this reason, this paper proposes the concept ofvisit intention, an indicator of the visual attention of a Web user in acertain period of time, short span time windows, in different areas ofinterest. This indicator gives information on the areas of interest of awebsite that will be visited by a user over a time window, without requiring toknow the structure of the site in each window. Our approach leverages thepopulation-level general gaze patterns and the user's visual kinetics. We showexperimentally that it is possible to conduct such a prediction throughmultilabel classification models using a small number of users, obtaining anaverage area under curve of 84.3 %, an average accuracy of 79 %, and anindividual area of interest accuracy of 77 %. Furthermore, the user's visualkinetics features are consistently selected in every set of a cross-validationevaluation.

Continuous Prediction of Web User Visual Attention on short span Windows based on Gaze Data Analytics