Strokes of insight: User intent detection and kinematic compression of mouse cursor trails

Abstract

Web users often have a specific goal in mind comprising various stages that are reflected, as executed, by their mouse cursor movements. Therefore, is it possible to detect automatically which parts of those movements bear any intent and discard the parts that have no intent? Can we estimate the intent degree of the non-discarded parts? To achieve this goal, we tap into the Kinematic Theory and its associated Sigma-Lognormal model (ΣΛM). According to this theory, the production of a mouse cursor movement requires beforehand the instantiation of an action plan. The ΣΛM models such an action plan as a sequence of strokes’ velocity profiles, one stroke at a time, providing thus a reconstruction of the original mouse cursor movement. When a user intent is clear, the pointing movement is faster and the cursor movement is reconstructed almost perfectly, while the reverse is observed when the user intent is unclear.We analyzed more than 10,000 browsing sessions comprising about 5 million of data points, and compared different segmentation techniques to detect discrete cursor chunks that were then reconstructed with the ΣΛM. Our main contribution is thus a novel methodology to automatically tell chunks with and without intention apart. We also contribute with kinematic compression, a novel application to compress mouse cursor data while preserving most of the original information. Ultimately, this work enables a deeper understanding of mouse cursor movements production, providing an informed means to gain additional insight about users’ browsing behavior.