Low-Level Activity Patterns as Indicators of User Familiarity withWebsites

Familiarity is a quality of user experience that has traditionally been difficult to define, capture, and quantify. Existing works on measuring familiarity with interactive systems have relied on surveys and self-reporting, which is obtrusive and prone to biases. Here, we propose a data-driven methodology to associate low-level activity patterns with familiarity. As a proof-of-concept, this methodology was tested on a website with 35,819 users over the course of 18 months, including 268 revisiting users who had reported their levels of familiarity with the platform. By using activity patterns as features of predictive models, we were able to successfully classify users with higher levels of familiarity with an accuracy of 82.7%. These results suggest that there is a relationship between user familiarity and activity patterns involving the exploration and use of navigational artefacts including breadcrumbs, navigation bars, and sidebar areas. This research opens up further opportunities for unobtrusively analysing the user experience on the Web.