Adaptive Graph-Based Uncertain Trajectory Data Augmentation Network for Next POI Recommendation

Next point-of-interest (POI) recommendation has shown effectiveness in mining complex user preferences and transition patterns from sparse check-in data. Existing methods generally leverage auxiliary information like spatial-temporal context, POI categories, and social relationships to alleviate the problem of data sparsity. However, most of them overlook the fact that the check-in records collected from users are often incomplete, leading to effective information missing and inadequately modeling. To this end, we propose a novel method Adaptive Graph-Based Trajectory Data Augmentation (AG-TDA) for next POI recommendation, which perceives potential relations among POIs and auxiliary information based on adaptive graph structure learning. Specifically, we design an adaptive graph-based trajectory data augmentation module from global view, which automatically explores implicit uncertain relations among POIs, categories, and regions via similarity learning with fine-grained node embeddings to get more expressive representations. In local view, we extend the self-attention mechanism by the learned fine-grained representations and personalized spatial-temporal information for capturing user dynamic intentions. Extensive experiments on several real-world datasets demonstrate the effectiveness of AG-TDA.