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In the realm of human mobility, the decision-making process for selecting thenext-visit location is intricately influenced by a trade-off between spatialand temporal constraints, which are reflective of individual needs andpreferences. This trade-off, however, varies across individuals, making themodeling of these spatial-temporal dynamics a formidable challenge. To addressthe problem, in this work, we introduce the "Spatial-temporal InducedHierarchical Reinforcement Learning" (STI-HRL) framework, for capturing theinterplay between spatial and temporal factors in human mobilitydecision-making. Specifically, STI-HRL employs a two-tiered decision-makingprocess: the low-level focuses on disentangling spatial and temporalpreferences using dedicated agents, while the high-level integrates theseconsiderations to finalize the decision. To complement the hierarchicaldecision setting, we construct a hypergraph to organize historical data,encapsulating the multi-aspect semantics of human mobility. We propose across-channel hypergraph embedding module to learn the representations as thestates to facilitate the decision-making cycle. Our extensive experiments ontwo real-world datasets validate the superiority of STI-HRL overstate-of-the-art methods in predicting users' next visits across variousperformance metrics.

Spatial-Temporal Interplay in Human Mobility: A Hierarchical Reinforcement Learning Approach with Hypergraph Representation