Deep convolutional recurrent model for region recommendation with spatial and temporal contexts

Spatiotemporal-aware region recommendation satisfies a user by providing an region of POIs (point-of -interests) that he/she may prefer. This recommendation is typically performed by analyzing the region mobility patterns of the user with some spatial and temporal contexts. This kind of recommendation can help, for example, a businessman to enjoy his urban life, or a tourist to travel in an unfamiliar area. In this study, we propose a deep-learning framework to model region-level mobility patterns of users, where personal and global user preferences across regions as well as spatiotemporal dependencies are comprehensively incorporated. To be specific, we model user preferences through a pyramidal Convolutional Long Short-Term Memory (ConvLSTM) component, and induce the dynamic region attributes through a recurrent component. By fusing two components to recommend next time region, our framework can tackle three complex challenges: (1) Modeling users' distinctive spatio-temporal preferences over regions; (2) tracing diverse region mobility patterns of users over time; and (3) capturing the intrinsic correlations between regions. Extensive experiments on real-world datasets validate the effectiveness of the novel approach.

Automated summary

The study proposes a deep-learning framework to model region-level mobility patterns of users, incorporating personal and global user preferences as well as spatiotemporal dependencies. By fusing different components to recommend the next time region, the framework effectively tackles three complex challenges: modeling users' spatiotemporal preferences, tracing region mobility patterns over time, and capturing correlations between regions.