  Identifying significant location categories visited by mobile users is the
key to a variety of applications. This is an extremely challenging task due to
the possible deviation between the estimated location coordinate and the actual
location, which could be on the order of kilometers. To estimate the actual
location category more precisely, we propose a novel tensor factorization
framework, through several key observations including the intrinsic
correlations between users, to infer the most likely location categories within
the location uncertainty circle. In addition, the proposed algorithm can also
predict where users are even in the absence of location information. In order
to efficiently solve the proposed framework, we propose a parameter-free and
scalable optimization algorithm by effectively exploring the sparse and
low-rank structure of the tensor. Our empirical studies show that the proposed
algorithm is both efficient and effective: it can solve problems with millions
of users and billions of location updates, and also provides superior
prediction accuracies on real-world location updates and check-in data sets.
