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

Negative-Unlabeled Tensor Factorization for Location Category Inference from Highly Inaccurate Mobility Data