  When users move in a physical space (e.g., an urban space), they would have
some records called mobility records (e.g., trajectories) generated by devices
such as mobile phones and GPS devices. Naturally, mobility records capture
essential information of how users work, live and entertain in their daily
lives, and therefore, they have been used in a wide range of tasks such as user
profile inference, mobility prediction and traffic management. In this paper,
we expand this line of research by investigating the problem of inferring user
socioeconomic statuses (such as prices of users' living houses as a proxy of
users' socioeconomic statuses) based on their mobility records, which can
potentially be used in real-life applications such as the car loan business.
For this task, we propose a socioeconomic-aware deep model called DeepSEI. The
DeepSEI model incorporates two networks called deep network and recurrent
network, which extract the features of the mobility records from three aspects,
namely spatiality, temporality and activity, one at a coarse level and the
other at a detailed level. We conduct extensive experiments on real mobility
records data, POI data and house prices data. The results verify that the
DeepSEI model achieves superior performance than existing studies. All datasets
used in this paper will be made publicly available.
