TrajGraph: A Dual-View Graph Transformer Model for Effective Next Location Recommendation

The next location recommendation is a significant task in spatio-temporal data mining (STDM), leading to an increased interest in the inherent dynamics in large-scale trajectory data. However, existing methods often prioritize the transition of locations while overlooking the collaborative signals between users, resulting in further impacts on modeling the higher-order effects between locations. Additionally, they struggle to extract useful patterns from long sequences, let alone capturing the long sequences that demonstrate the collaborative effects between users and locations. In light of these challenges, we construct a temporal graph based on the order of user movement, enabling both users and locations to utilize collaborative filtering signals. This greatly alleviates issues related to data sparsity and high-quality representation. Simultaneously, we introduce a Dual-View Graph Transformer model(TrajGraph), which samples sequences from spatial and temporal views by a dual-view sequence sampling method, independently encoding each view with a graph transformer to obtain effective representations of visited nodes, effectively addressing the high complexity of the transformer and ensuring efficiency and effectiveness. Extensive experiments on three public location-based service datasets demonstrate that our model can consistently outperform all baselines. Elaborate ablation studies further prove the effectiveness of spatial and temporal factors.