Spot: Selecting occupations from trajectories

With the pervasive availability of smart devices, billions of users' trajectories are recorded and collected. The aggregated human behaviors reveal users' interests and characteristics, becoming invaluable to reflect their demographic preference, i.e., gender, age, marital status and even personality, occupation. Occupation profiling from trajectory data is an attractive option for advertisement targeting and other applications, without severe privacy concerns. However, it carries great difficulties in sparsity and vagueness. This paper proposes a novel approach, i.e., SPOT (Selecting occu-Pation frOm Trajectories). We first carefully analyze and report the trajectory pattern variance of different occupational categories in a large real dataset. And then we design novel ways to extract users content, location and transition preference, and finally illustrate a comprehensive occupation prediction method, Continuous Conditional Random Fields (C-CRF) based prediction model. Empirical studies confirm that the new approach works surprisingly well, and it shows the discriminative power of trajectory data to reveal occupational preference.