Estimating users' home and work locations leveraging large-scale crowd-sourced smartphone data


Estimating the home and work locations of users is very important for applications like city coming up with and personalized recommendations. Although existing approaches can achieve a cheap precision, they depend upon fine-grained sensor information with high sampling rate. Thus, these approaches come with a high price and are only studied in little samples of volunteers and so cannot benefit giant-scale Web users. In this article we have a tendency to propose a technique to use crowd-sourced location information from mobile devices to estimate the home and work locations of large-scale users, leveraging the computation power of the cloud. Experimental results demonstrate our approach achieves a smart estimation precision. Moreover, we have a tendency to more study how the estimated home and work locations can be employed in two typical applications that are difficult problems using traditional ways but can be elegantly solved by leveraging the proposed crowd-sourced approach.

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