  In recent years, a vast amount of research has been conducted on learning
people's interests from their actions. Yet their collective actions also allow
us to learn something about the world, in particular, infer attributes of
places people visit or interact with. Imagine classifying whether a hotel has a
gym or a swimming pool, or whether a restaurant has a romantic atmosphere
without ever asking its patrons. Algorithms we present can do just that.
  Many web applications rely on knowing attributes of places, for instance,
whether a particular restaurant has WiFi or offers outdoor seating. Such data
can be used to support a range of user experiences, from explicit query-driven
search to personalized place recommendations. However, obtaining these
attributes is generally difficult, with existing approaches relying on
crowdsourcing or parsing online reviews, both of which are noisy, biased, and
have limited coverage. Here we present a novel approach to classifying place
attributes, which learns from patrons' visit patterns based on anonymous
observational data.
  Our method, STEPS, learns from aggregated sequences of place visits. For
example, if many people visit the restaurant on a Saturday evening, coming from
a luxury hotel or theater, and stay for a long time, then this restaurant is
more likely to have a romantic atmosphere. On the other hand, if most people
visit the restaurant on weekdays, coming from work or a grocery store, then the
restaurant is less likely to be romantic. We show that such transition features
are highly predictive of place attributes. In an extensive empirical
evaluation, STEPS nearly doubled the coverage of a state of the art approach
thanks to learning from observational location data, which allowed our method
to reason about many more places.
