GeoMamba: Towards Multi-Granular POI Recommendation with Geographical State Space Model

Abstract

Point-of-Interest (POI) recommendation plays an important role in a wide range of location-based social network ap- plications, aiming to accurately predicting users’ next visits based on their historical check-in records. Previous efforts have primarily focused on the modifications of existing sequential models, neglecting the fact that POI visiting sequences typically involve continuous state transformation of geographical and intention signals. Additionally, the diverse time span between check-ins require the model to prop- erly recognize user’s multi-granular preference. While recent advances of State Space Model (SSM) have revealed their potential in handling intricate temporal signals, we propose a state-based model that is tailored for spatio-temporal POI sequences. On top of traditional SSMs that are typically limited to linear sequences like Mamba, we propose GeoMamba, which customizes the model states to accommodate the spatio-temporal sequences, especially fitting for POI recommendations. Specifically, while the approximation operator HiPPO sets the foundation of linear SSMs, we introduce a novel GaPPO operator that extends the model’s state space into graph-represented geographical domains. This innovation allows us to construct locational SSM encoders that seamlessly integrate users’ spatio-temporal characteristics. The sequence-aware outputs of GeoMamba are further processed to generate multi-scale behavior representations. Extensive experimental results illustrate the superiority of GeoMamba over several state-of-the-art baselines.