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.

翻译：位置推荐在提升用户旅行体验中起着关键作用。待预测兴趣点的时间戳具有重要意义，因为用户在不同时间会前往不同地点。然而，现有方法大多未利用此类时间信息，或仅将其与其他上下文信息隐式融合。本文重新审视位置推荐问题，指出当模型需要预测不仅包含下一位置，还包括更远位置时，明确建模时间信息具有显著帮助。此外，现有最先进方法未能有效利用地理信息，且在通过网格化编码地理信息时存在硬边界问题。为此，本文提出一种基于时间提示的地理感知（TPG）框架。首先设计时间提示以融合任意未来签到的时序信息，继而提出移位窗口机制增强地理数据以解决硬边界问题。通过与现有方法的广泛对比及基于五个真实数据集的消融实验，我们验证了所提方法在多种场景下的有效性与优越性。最重要的是，本文模型具备卓越的区间预测能力：该模型可在掩盖最近签到行为数据的情况下预测用户在特定时间想前往的位置，或根据给定时间戳预测具体未来签到（而不仅限于下一签到）。