mlsanityData to 7 Oct 2026

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

arXiv 2410.02191 ↗First posted 3 Oct 2024cs.IR cs.AI cs.CEcited by 12

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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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 58 of its 180 references that are in the map. First posted on arXiv 3 Oct 2024.

Builds on

58 of 180 references.

References · 58

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38 more
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  16. 2020A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendationscited by 16
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  18. 2024A Survey of Generative Techniques for Spatial-Temporal Data Miningcited by 14
  19. 2022Decentralized Collaborative Learning Framework for Next POI Recommendationcited by 13
  20. 2024Semantic Trajectory Data Mining with LLM-Informed POI Classificationcited by 13
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50 most cited of 58 All 58 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 6; 2026: 5.

12 papers.

  1. 2025Enhancing Large Language Models for Mobility Analytics with Semantic Location Tokenizationcited by 8
  2. 2025AutoHFormer: Efficient Hierarchical Autoregressive Transformer for Time Series Predictioncited by 7
  3. 2025OneLoc: Geo-Aware Generative Recommender Systems for Local Life Servicecited by 5
  4. 2025Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarkscited by 3
  5. 2024HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learningcited by 2
  6. 2026MODE: Efficient Time Series Prediction with Mamba Enhanced by Low-Rank Neural ODEscited by 1
  7. 2025Disentangling Multiplex Spatial-Temporal Transition Graph Representation Learning for Socially Enhanced POI Recommendationcited by 0
  8. 2025QoSDiff: An Implicit Topological Embedding Learning Framework Leveraging Denoising Diffusion and Adversarial Attention for Robust QoS Predictioncited by 0
  9. 2026Efficient Prompt Learning for Traffic Forecastingcited by 0
  10. 2026POI Recommendation with LLM-Augmented Multi-Graph Learning and Contrastive Alignmentcited by 0
  11. 2026An Empirical Evaluation of Cross-City POI Recommendation on a Large-Scale Benchmarkcited by 0
  12. 2026Eval4DiRec: A Unified and Systematic Evaluation Framework for Diffusion-based Recommender Systemscited by 0