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Today's Internet services increasingly use IP-based geo-location to specialize the content and service provisioning for each user. However, these systems focus almost exclusively on the current position of users and do not attempt to infer or exploit any qualitative context about the location's relationship with the user (e.g., is the user at home? on a business trip?). This paper develops such a context by profiling the usage patterns of IP address ranges, relying on known user and machine identifiers to track accesses over time. The preliminary results suggest that rough location categories such as residences, workplaces, and travel venues can be accurately inferred, enabling a range of potential applications from demographic analyses to ad specialization and security improvements.
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