Negative-Unlabeled Tensor Factorization for Location Category Inference from Highly Inaccurate Mobility Data
This work addresses the challenge of precise location category inference for mobile applications, offering a scalable solution with practical impact, though it appears incremental as it builds on tensor factorization methods.
The paper tackles the problem of inferring location categories from inaccurate mobility data by proposing a tensor factorization framework that leverages user correlations and location uncertainty circles, achieving superior prediction accuracies on real-world datasets with scalability to millions of users and billions of location updates.
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.