Joint Prediction Method for User and Point-of-Interest Based on Overall Spatio-Temporal Consistency Perception

In social networks, users' mobile behavior is jointly driven by temporal periodicity, geographical proximity, and semantic category preferences. However, interaction data are often highly sparse. Existing methods mostly focus on modeling user sequences, often failing to uniformly capture and ensure the complex consistency associations among the above spatiotemporal semantic factors, resulting in insufficient robustness of the learned patterns from sparse data. Therefore, this paper proposes the concept of overall spatiotemporal consistency, which comprehensively considering the temporal and spatial consistency at each stage of the user and Point-of-Interest (POI) joint prediction task, to achieve collaborative geography-wise and category-wise prediction. Specifically, this study considers the three-dimensional feature space of time, geographical coordinates, and semantic categories, as well as the temporal consistency between geography-time and category-time space and the spatial consistency between geography-category space. Corresponding consistency constraints are introduced in the feature space embedding, influence representation, influence decoupling, and influence-based fusion inference stages to construct an improved disentangled graph embedding prediction model. The model first introduces a spatial consistency constraint based on the aggregation dependency between geography-category embeddings. Then, it uses a graph neural network to extract five types of influence factors and achieves disentangled learning based on temporal consistency through a time-space dual-domain parallel influence decoupling method. Finally, it obtains semantic category prediction based on the geographical coordinate prediction results and the category aggregation dependencies, interacting with spatial consistency between geographical and categorical dimensions. Experimental results demonstrate that the proposed method is superior to baseline models on the Foursquare dataset. Removing the embedding layer aggregation module reduces the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) and log loss of the prediction task by 6.13% and 36.29%, respectively, compared with the best baseline. This is a highly efficient spatiotemporal semantic multi-consistency modeling approach. The gain of the inference layer aggregation module is related to the data scale and can provide fine-grained adjustments to the prediction results. The temporal feature module can provide important behavioral prior information for the model under the condition of sparse check-in data.