# STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation

arXiv: https://arxiv.org/abs/2010.07024 · HTML: https://mlsanity.com/paper/2010.07024/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 30 of 31)

- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)
- [A Survey on Graph Representation Learning Methods](https://mlsanity.com/paper/2204.01855/) (2022)
- [Large Language Models for Next Point-of-Interest Recommendation](https://mlsanity.com/paper/2404.17591/) (2024)
- [A Diffusion model for POI recommendation](https://mlsanity.com/paper/2304.07041/) (2023)
- [Counterfactual Learning on Graphs: A Survey](https://mlsanity.com/paper/2304.01391/) (2023)
- [Graph-adaptive Rectified Linear Unit for Graph Neural Networks](https://mlsanity.com/paper/2202.06281/) (2022)
- [DynSTGAT: Dynamic Spatial-Temporal Graph Attention Network for Traffic Signal Control](https://mlsanity.com/paper/2109.05491/) (2021)
- [Revisiting Mobility Modeling with Graph: A Graph Transformer Model for Next Point-of-Interest Recommendation](https://mlsanity.com/paper/2310.01224/) (2023)
- [Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax](https://mlsanity.com/paper/2212.05707/) (2022)
- [LIMP: Large Language Model Enhanced Intent-aware Mobility Prediction](https://mlsanity.com/paper/2408.12832/) (2024)
- [Enhancing Large Language Models for Mobility Analytics with Semantic Location Tokenization](https://mlsanity.com/paper/2506.11109/) (2025)
- [Geo-Llama: Leveraging LLMs for Human Mobility Trajectory Generation with Spatiotemporal Constraints](https://mlsanity.com/paper/2408.13918/) (2024)
- [Predicting Human Mobility via Self-supervised Disentanglement Learning](https://mlsanity.com/paper/2211.09625/) (2022)
- [Empowering Next POI Recommendation with Multi-Relational Modeling](https://mlsanity.com/paper/2204.12288/) (2022)
- [Hyper-Relational Knowledge Graph Neural Network for Next POI](https://mlsanity.com/paper/2311.16683/) (2023)
- [Towards Effective Next POI Prediction: Spatial and Semantic Augmentation with Remote Sensing Data](https://mlsanity.com/paper/2404.04271/) (2024)
- [Beyond Regularity: Modeling Chaotic Mobility Patterns for Next Location Prediction](https://mlsanity.com/paper/2509.11713/) (2025)
- [Activity-aware Human Mobility Prediction with Hierarchical Graph Attention Recurrent Network](https://mlsanity.com/paper/2210.07765/) (2022)
- [Self-supervised Graph-based Point-of-interest Recommendation](https://mlsanity.com/paper/2210.12506/) (2022)
- [HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization](https://mlsanity.com/paper/2405.08043/) (2024)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)
- [Spacetime-GR: A Spacetime-Aware Generative Model for Large Scale Online POI Recommendation](https://mlsanity.com/paper/2508.16126/) (2025)
- [Sequence Adaptation via Reinforcement Learning in Recommender Systems](https://mlsanity.com/paper/2108.01442/) (2021)
- [A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation](https://mlsanity.com/paper/2201.08150/) (2022)
- [Bi-Level Graph Structure Learning for Next POI Recommendation](https://mlsanity.com/paper/2411.01169/) (2024)
- [ADS-POI: Agentic Spatiotemporal State Decomposition for Next Point-of-Interest Recommendation](https://mlsanity.com/paper/2604.20846/) (2026)
- [Joint Triplet Loss Learning for Next New POI Recommendation](https://mlsanity.com/paper/2209.12162/) (2022)
- [Enhancing POI Recommendation through Global Graph Disentanglement with POI Weighted Module](https://mlsanity.com/paper/2507.14612/) (2025)
- [STRelay: A Universal Spatio-Temporal Relaying Framework for Location Prediction over Human Trajectory Data](https://mlsanity.com/paper/2508.16620/) (2025)
- [Spatio-Temporal Graphs Beyond Grids: Benchmark for Maritime Anomaly Detection](https://mlsanity.com/paper/2512.20086/) (2025)

## Abstract (arXiv)

Next Point-of-Interest (POI) recommendation is a longstanding problem across the domains of Location-Based Social Networks (LBSN) and transportation. Recent Recurrent Neural Network (RNN) based approaches learn POI-POI relationships in a local view based on independent user visit sequences. This limits the model's ability to directly connect and learn across users in a global view to recommend semantically trained POIs. In this work, we propose a Spatial-Temporal-Preference User Dimensional Graph Attention Network (STP-UDGAT), a novel explore-exploit model that concurrently exploits personalized user preferences and explores new POIs in global spatial-temporal-preference (STP) neighbourhoods, while allowing users to selectively learn from other users. In addition, we propose random walks as a masked self-attention option to leverage the STP graphs' structures and find new higher-order POI neighbours during exploration. Experimental results on six real-world datasets show that our model significantly outperforms baseline and state-of-the-art methods.