User Profiling for Urban Computing: Enriching Social Network Trace Data

Abstract

Location-Based Social Networks (LBSNs), with their huge amount of geo-located user generated content, are providing a lot of semantics on human mobility and behaviour as well as on users' interests and activities in cities. In this paper we propose an innovative approach to detect city zones and reveal city dynamics which exploits clustering techniques based on an original feature selection. We also present the results in LiveCities\footnote{Video available at http://vimeo.com/miccunifi/livecities}, a web application designed adopting new information visualisations paradigms in order to easily get cities' insights. Recommendation of city zones and venues close to user's interests, based on semi-automatic user profiling, is also provided exploiting semantic similarity algorithms. Results, validated by a case study on the city of Florence (Italy) through an online questionnaire filled out by residents, show that our feature performs better than traditional approaches.