Automatic Categorization of Users Based on their Web Navigation

The aim of this work is to create an automated method of categorizing users according to their cognitive styles. That is why we need to get enough data on how users of different cognitive styles are behaving on the web. Based on these, we will design and verify a classifier that can properly assign a cognitive style to the user. It is proved that users with different cognitive style perceive and process the information provided to them differently. By automatically determining a user’s cognitive style only during his web browsing, it would be possible to start personalizing a site that would be better suited to his website perception, thereby improving his user experience and browsing speed.

Two dimensions of cognitive styles were described and verified in psychological research in the past, specifically Verbal-Imager and Wholist-Analyst dimensions. At the beginning of the experiment, we will perform a short test to determine user’s inclusion to these two cognitive style dimensions. The experiment will then continue with search-based tasks on the Web. These are designed to detect differences in users navigating behavior of different cognitive styles. For the classifier to work properly, it is therefore necessary to collect enough data from eye-tracking, but also multiple metrics measured by Javascript on the front-end.