Movement-based User Profiling

Personalization is a key issue for Location-Based Services (LBS), with users increasingly expecting their devices and services to be tailored and adjusted to their specific needs and preferences. This is particularly relevant for systems aiming to support sustainable mobility behavior, such as eco-feedback applications or multi-modal route planners and navigation services.

A prerequisite for such adaptation, however, is the generation of user profiles. In the MIE Lab, we explore potential methods to derive the necessary information from analyzing and mining spatio-temporal movement data.