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:地理位置推荐在改善用户旅行体验方面起着至关重要的作用。待预测的POI的时间戳非常重要,因为用户在不同时刻会去不同的地方。然而,大多数现有方法要么不使用这种时间信息,要么只是将其隐式地融合到其他上下文信息中。在本文中,我们重新审视了地理位置推荐问题,并指出在模型需要预测不仅是下一个位置,还需要预测更多位置时,显式地建模时间信息是有很大帮助的。此外,最先进的方法没有有效地利用地理信息,并且在通过网格化编码地理信息时遇到了硬边界问题。为此,我们提出了一种基于时间提示和地理感知的(TPG)框架。首先设计了时间提示,以包含任何进一步签到的时间信息。然后,设计了位移窗口机制来增加地理数据,以解决硬边界问题。通过在五个真实数据集上进行广泛的比较和消融研究,我们展示了所提出的方法在各种情况下的有效性和优越性。最重要的是,我们提出的模型具有间隔预测的优越能力。特别是,该模型可以在屏蔽最近的签到行为数据时,预测用户在某个特定时间想去哪个位置,或者可以在给定时间戳时预测具体的未来签到(不仅仅是下一个)。