Modeling dynamic spatiotemporal user preference for location prediction: a mutually enhanced method

As the cornerstone of location-based services, location prediction aims to predict user’s next location through modeling user’s personal preference or travel sequential pattern. However, most existing methods only consider one of them and extremely sparse data makes it difficult to dynamically and comprehensively characterize user preference. In this paper, we propose a novel D ynamic S patiotemporal U ser P reference (DSUP) model to characterize dynamic spatiotemporal user preference and integrate it with user’s travel sequential pattern for location prediction. Specifically, we design an interaction-aware graph attention network to learn the embeddings of locations and timeslots, and infer dynamic spatiotemporal user preference from the history travel locations and timeslots. Then, we combine user’s current travel preference with the impact of history travel sequential pattern to predict user’s next location. In addition, we predict user’s next travel timeslot and combine it with the temporal pattern of locations to enhance the location and timeslot prediction results mutually. We conduct extensive experiments on two public datasets Gowalla, Foursquare and our own Private Car dataset. The results on three datasets show that our method improves the accuracy and mean reciprocal rank of location prediction by 3%-11% and 7%-10% respectively.