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

## TL;DR

This paper introduces a tensor factorization method that leverages user correlations to accurately infer location categories from highly inaccurate mobility data, outperforming existing approaches in efficiency and accuracy.

## Contribution

It presents a novel scalable tensor factorization framework that effectively handles large-scale, noisy mobility data for location inference, incorporating intrinsic user correlations.

## Key findings

- Efficiently handles millions of users and billions of location updates.
- Achieves superior prediction accuracy on real-world datasets.
- Provides a scalable, parameter-free optimization algorithm.

## Abstract

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.