Learning career mobility and human activity patterns for job change analysis

Discovering the determinants of job change and predicting the individual job change occasion are essential approaches for understanding the professional careers of human. However, with the evolution of labor division and globalization, modern careers become more self-directed and dynamic, which makes job change occasion difficult to predict. Fortunately, the emerging online professional networks and location-based social networks provide a large amount of work experience and daily activity records of individuals around the world, which open a venue for the accurate job change analysis. Discovering the determinants of job change and predicting the individual job change occasion are essential approaches for understanding the professional careers of human. However, with the evolution of labor division and globalization, modern careers become more self-directed and dynamic, which makes job change occasion difficult to predict. Fortunately, the emerging online professional networks and location-based social networks provide a large amount of work experience and daily activity records of individuals around the world, which open a venue for the accurate job change analysis. In this paper, we aggregate the work experiences and check-in records of individuals to model the job change motivations and correlations between professional and daily life. Specifically, we attempt to reveal to what extent the job change occasion can be predicted based on the career mobility and daily activity patterns at the individual level. Following the classical theory of job mobility determinants, we extract and quantify the environmental conditions and personal preference of careers from the perspective of industrial/regional constraints and personal interests/demands. Besides, we investigate the factors of activity patterns which may be correlated with job change as cause and effect results. First, we quantify the consumption diversity, sentiment fluctuation and geographic movement from the check-in records as indicators. Then, we leverage the center-bias level assignment and multi-point snapshot mechanism to capture historical and parallel migration. Finally, experimental results based on a large real-world dataset show that the job change occasions can be accurately predicted with the aggregated factors.