COAST: Contrastive Learning with Augmented Spatio-Temporal Encoding for Next POI Recommendation

Next point-of-interest (POI) recommendations have garnered significant attention in industry and academia due to their crucial role in location-based social networks (LBSNs). Recent approaches have integrated sequence and geographical data to improve recommendation accuracy. However, traditional methods do not explicitly learn user similarity, which may result in suboptimal POI prediction outcomes. To address these limitations, we propose the contrastive learning with augmented spatio-temporal(COAST) model, which more effectively utilizes user check-in sequences and geographical influences. Our approach includes five techniques for augmenting check-in records and a novel Two-Head Self-Attention Encoder (THSE) to capture spatio-temporal and structural patterns. Extensive experiments on three real-world datasets demonstrate the superiority of our model compared to existing methods.