  In this paper, we study the problem of mobile user profiling, which is a
critical component for quantifying users' characteristics in the human mobility
modeling pipeline. Human mobility is a sequential decision-making process
dependent on the users' dynamic interests. With accurate user profiles, the
predictive model can perfectly reproduce users' mobility trajectories. In the
reverse direction, once the predictive model can imitate users' mobility
patterns, the learned user profiles are also optimal. Such intuition motivates
us to propose an imitation-based mobile user profiling framework by exploiting
reinforcement learning, in which the agent is trained to precisely imitate
users' mobility patterns for optimal user profiles. Specifically, the proposed
framework includes two modules: (1) representation module, which produces state
combining user profiles and spatio-temporal context in real-time; (2) imitation
module, where Deep Q-network (DQN) imitates the user behavior (action) based on
the state that is produced by the representation module. However, there are two
challenges in running the framework effectively. First, epsilon-greedy strategy
in DQN makes use of the exploration-exploitation trade-off by randomly pick
actions with the epsilon probability. Such randomness feeds back to the
representation module, causing the learned user profiles unstable. To solve the
problem, we propose an adversarial training strategy to guarantee the
robustness of the representation module. Second, the representation module
updates users' profiles in an incremental manner, requiring integrating the
temporal effects of user profiles. Inspired by Long-short Term Memory (LSTM),
we introduce a gated mechanism to incorporate new and old user characteristics
into the user profile.
