Finding Important Locations: A Feature-Based Approach

We propose a novel approach to address the problem of the recognition of important locations. Our method is organised in two phases: first, a set of candidate stay points is identified by exploiting some state-of-the-art algorithms to filter the GPS-logs, then, the candidate stay points are mapped onto a feature space having as dimensions the area underlying the stay point, its intensity (the time spent in a location) and its frequency (the number of total visits). We conjecture that the features space allows to model aspects/measures that are more semantically related to users and better suited to reason about their similarities and differences than, e.g., Latitude, longitude, and timestamp. An experimental evaluation on the GeoLife public dataset confirms the effectiveness of our approach.