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