# Timestamps as Prompts for Geography-Aware Location Recommendation

arXiv: https://arxiv.org/abs/2304.04151 · HTML: https://mlsanity.com/paper/2304.04151/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2023.

## Built on by (most cited first, 6 of 6)

- [Cross-modal Prompts: Adapting Large Pre-trained Models for Audio-Visual Downstream Tasks](https://mlsanity.com/paper/2311.05152/) (2023)
- [Taming the Long Tail in Human Mobility Prediction](https://mlsanity.com/paper/2410.14970/) (2024)
- [OneLoc: Geo-Aware Generative Recommender Systems for Local Life Service](https://mlsanity.com/paper/2508.14646/) (2025)
- [Lightweight yet Efficient: An External Attentive Graph Convolutional Network with Positional Prompts for Sequential Recommendation](https://mlsanity.com/paper/2502.15331/) (2025)
- [OsmT: Bridging OpenStreetMap Queries and Natural Language with Open-source Tag-aware Language Models](https://mlsanity.com/paper/2512.04738/) (2025)
- [Why Users Go There: World Knowledge-Augmented Generative Next POI Recommendation](https://mlsanity.com/paper/2605.11807/) (2026)

## Abstract (arXiv)

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.