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Location recommendation plays a vital role in improving users' travelexperience. The timestamp of the POI to be predicted is of great significance,since a user will go to different places at different times. However, mostexisting methods either do not use this kind of temporal information, or justimplicitly fuse it with other contextual information. In this paper, we revisitthe problem of location recommendation and point out that explicitly modelingtemporal information is a great help when the model needs to predict not onlythe next location but also further locations. In addition, state-of-the-artmethods do not make effective use of geographic information and suffer from thehard boundary problem when encoding geographic information by gridding. To thisend, a Temporal Prompt-based and Geography-aware (TPG) framework is proposed.The temporal prompt is firstly designed to incorporate temporal information ofany further check-in. A shifted window mechanism is then devised to augmentgeographic data for addressing the hard boundary problem. Via extensivecomparisons with existing methods and ablation studies on five real-worlddatasets, we demonstrate the effectiveness and superiority of the proposedmethod under various settings. Most importantly, our proposed model has thesuperior ability of interval prediction. In particular, the model can predictthe location that a user wants to go to at a certain time while the most recentcheck-in behavioral data is masked, or it can predict specific future check-in(not just the next one) at a given timestamp.

Timestamps as Prompts for Geography-Aware Location Recommendation