AST-PG: Attention-Based Spatial–Temporal Point-of-Interest-Group Model for Real-Time Point-of-Interest Recommendation

Research on next-point-of-interest (POI) recommendation has become a new focus in the field of POI recommendation in recent years. The goal of POI recommendation tasks is to predict a user’s future movement trajectory based on their current state and historical behavioral information. Recent studies have shown the effectiveness of neural network-based next-POI recommendation engines. However, most existing models only consider the correlation between consecutive visits, neglecting the complex dependencies of the POIs in the area and category features, as well as the processing of unstructured time series. This paper presents a new Attention-Based Spatial–Temporal Point-of-Interest-Group (AST-PG) model for POI recommendation. The model consists of a spatial module and a temporal module combined with each other by a multiple-attention mechanism. The spatial module in the proposed model groups the POIs based on geographic and category features, while the temporal module develops a uniform-length time trajectory vector for the unstructured temporal features. Comprehensive experimental results on two real datasets demonstrate that the proposed model of this study is superior to the state-of-the-art POI recommendation models in terms of performance.