  With the growth of using cell phones and the increase in diversity of smart
mobile devices, a massive volume of data is generated continuously in the
process of using these devices. Among these data, Call Detail Records, CDR, is
highly remarkable. Since CDR contains both temporal and spatial labels,
mobility analysis of CDR is one of the favorite subjects of study among the
researchers. The user next location prediction is one of the main problems in
the field of human mobility analysis. In this paper, we propose a data
processing framework to predict user next location. We propose domain-specific
data processing strategies and design a deep neural network model which is
based on recurrent neurons and perform regression tasks. Using this prediction
framework, the error of the prediction decreases from 74% to 55% in comparison
to the worst and best performing traditional models. Methods, strategies, the
framework and the results of this paper can be helpful in many applications
such as urban planning and digital marketing.
