Today’s Internet services increasingly use IP-based geolocation
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. Our 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