# A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security

arXiv: https://arxiv.org/abs/2410.02191 · HTML: https://mlsanity.com/paper/2410.02191/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 58 of its 180 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 30 of 58)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends](https://mlsanity.com/paper/2301.05712/) (2023)
- [Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2402.00789/) (2024)
- [A Survey of Multi-View Representation Learning](https://mlsanity.com/paper/1610.01206/) (2016)
- [Self-Supervised Learning for Recommender Systems: A Survey](https://mlsanity.com/paper/2203.15876/) (2022)
- [GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation](https://mlsanity.com/paper/2303.04741/) (2023)
- [Large Language Models for Next Point-of-Interest Recommendation](https://mlsanity.com/paper/2404.17591/) (2024)
- [PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion](https://mlsanity.com/paper/2110.10926/) (2021)
- [A Diffusion model for POI recommendation](https://mlsanity.com/paper/2304.07041/) (2023)
- [Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation](https://mlsanity.com/paper/2202.04893/) (2022)
- [FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling](https://mlsanity.com/paper/2202.04975/) (2022)
- [FedRecAttack: Model Poisoning Attack to Federated Recommendation](https://mlsanity.com/paper/2204.01499/) (2022)
- [On-Device Recommender Systems: A Comprehensive Survey](https://mlsanity.com/paper/2401.11441/) (2024)
- [Where to Move Next: Zero-shot Generalization of LLMs for Next POI Recommendation](https://mlsanity.com/paper/2404.01855/) (2024)
- [A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations](https://mlsanity.com/paper/2011.10187/) (2020)
- [Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense](https://mlsanity.com/paper/2212.05399/) (2022)
- [A Survey of Generative Techniques for Spatial-Temporal Data Mining](https://mlsanity.com/paper/2405.09592/) (2024)

## Built on by (most cited first, 12 of 12)

- [Enhancing Large Language Models for Mobility Analytics with Semantic Location Tokenization](https://mlsanity.com/paper/2506.11109/) (2025)
- [AutoHFormer: Efficient Hierarchical Autoregressive Transformer for Time Series Prediction](https://mlsanity.com/paper/2506.16001/) (2025)
- [OneLoc: Geo-Aware Generative Recommender Systems for Local Life Service](https://mlsanity.com/paper/2508.14646/) (2025)
- [Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks](https://mlsanity.com/paper/2505.11239/) (2025)
- [HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning](https://mlsanity.com/paper/2410.10915/) (2024)
- [MODE: Efficient Time Series Prediction with Mamba Enhanced by Low-Rank Neural ODEs](https://mlsanity.com/paper/2601.00920/) (2026)
- [Eval4DiRec: A Unified and Systematic Evaluation Framework for Diffusion-based Recommender Systems](https://mlsanity.com/paper/2609.34404/) (2026)
- [Disentangling Multiplex Spatial-Temporal Transition Graph Representation Learning for Socially Enhanced POI Recommendation](https://mlsanity.com/paper/2508.07649/) (2025)
- [QoSDiff: An Implicit Topological Embedding Learning Framework Leveraging Denoising Diffusion and Adversarial Attention for Robust QoS Prediction](https://mlsanity.com/paper/2512.04596/) (2025)
- [Efficient Prompt Learning for Traffic Forecasting](https://mlsanity.com/paper/2605.08273/) (2026)
- [POI Recommendation with LLM-Augmented Multi-Graph Learning and Contrastive Alignment](https://mlsanity.com/paper/2608.16407/) (2026)
- [An Empirical Evaluation of Cross-City POI Recommendation on a Large-Scale Benchmark](https://mlsanity.com/paper/2608.27840/) (2026)

## Abstract (arXiv)

The widespread adoption of smartphones and Location-Based Social Networks has led to a massive influx of spatio-temporal data, creating unparalleled opportunities for enhancing Point-of-Interest (POI) recommendation systems. These advanced POI systems are crucial for enriching user experiences, enabling personalized interactions, and optimizing decision-making processes in the digital landscape. However, existing surveys tend to focus on traditional approaches and few of them delve into cutting-edge developments, emerging architectures, as well as security considerations in POI recommendations. To address this gap, our survey stands out by offering a comprehensive, up-to-date review of POI recommendation systems, covering advancements in models, architectures, and security aspects. We systematically examine the transition from traditional models to advanced techniques such as large language models. Additionally, we explore the architectural evolution from centralized to decentralized and federated learning systems, highlighting the improvements in scalability and privacy. Furthermore, we address the increasing importance of security, examining potential vulnerabilities and privacy-preserving approaches. Our taxonomy provides a structured overview of the current state of POI recommendation, while we also identify promising directions for future research in this rapidly advancing field.