Combining eye tracking and pupillary dilation analysis to identify Website Key Objects

Abstract

Identifying the salient zones from Web interfaces, namely the Website Key Objects, is an essential part of the personalization process that current Web systems perform to increase user engagement. While several techniques have been proposed, most of them are focused on the use of Web usage logs. Only recently has the use of data from users' biological responses emerged as an alternative to enrich the analysis. In this work, a model is proposed to identify Website Key Objects that not only takes into account visual gaze activity, such as fixation time, but also the impact of pupil dilation. Our main hypothesis is that there is a strong relationship in terms of the pupil dynamics and the Web user preferences on a Web page. An empirical study was conducted on a real Website, from which the navigational activity of 23 subjects was captured using an eye tracking device. Results showed that the inclusion of pupillary activity, although not conclusively, allows us to extract a more robust Web Object classification, achieving a 14% increment in the overall accuracy.