GeM: Gaussian embeddings with Multi-hop graph transfer for next POI recommendation

Abstract

Next Point-of-Interest (POI) recommendation is crucial in location-based applications, analyzing user behavior patterns from historical trajectories. Existing studies usually use graph structures and attention mechanisms for sequential prediction with single fixed points. However, existing work based on the Markov chain hypothesis neglects dependencies of multi-hop transfers between POIs, which is a common pattern of user behaviors. To address these limitations, we propose GeM, a unified framework that effectively employs Gaussian distribution and Multi-hop graph relation to capture movement patterns and simulate user travel decisions, considering user preference and objective factors simultaneously. At the subjective module, Gaussian embeddings with Mahalanobis distance are exploited to make the embedded space non-flat and stable, which enables the expression of asymmetric relations, while the objective module also mines graph information and multi-hop dependency through a global trajectory graph, reflecting POI associations with user movement. Besides, matrix factorization is used to learn user-POI interaction. By combining both modules, we get a more accurate representation of user behavior patterns. Extensive experiments conducted on two real-world datasets show that our model outperforms the compared state-of-the-art methods.