Modeling Dynamic Social Behaviors with Time-Evolving Graphs for User Behavior Predictions

The full coverage of Wi-Fi signals and the popularization of intelligent card systems provide a large volume of data that contain human mobility patterns. Effectively utilizing such data to make user behavior predictions finds useful applications such as predictive behavior analysis, personalized recommendation, and location-aware services. Existing methods for user behavior predictions merely capture temporal dependencies within individual historical records. We argue that user behaviors are largely affected by friends in their social circles and such influences are dynamic due to users' dynamic social behaviors. In this paper, we propose a model named SDSIM which consists of three independent and complementary modules to jointly model the influences of user dynamic social behaviors, user demographics similarities, and individual-level behavior patterns. We construct time-evolving graphs to indicate user dynamic social behaviors and design a novel component named DSBcell which captures not only the social influences but also the regularity and periodicity of user social behaviors. We also construct a graph based on user similarities in demographics and generate a representation for each user. Experiments on two real-world datasets for multiple user behavior-related prediction tasks prove the effectiveness of our proposed model compared with state-of-the-art methods.