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

TL;DR

The paper addresses POI recommendation by proposing a comprehensive taxonomy across three interrelated axes: models, architectures, and security. It surveys a progression from traditional latent-factor and neural models to self-supervised and generative approaches, including LLMs and diffusion/VAE techniques, and tracks architectural shifts from centralized to decentralized and federated learning frameworks. It highlights security as a central concern, detailing data integrity threats and privacy-preserving methods, and presents future directions to balance performance, scalability, and privacy in real-world systems. The work aims to guide researchers and practitioners by mapping current methods, identifying gaps, and suggesting actionable directions for robust, scalable, and explainable POI recommendations.

Abstract

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.

Figures (7)

• Figure 1: Taxonomy of existing studies for POI recommendation in terms of models, architecture and security
• Figure 2: Overview of POI recommendation studies.
• Figure 3: The Transformer model architecture
• Figure 4: GNN and DM model structures.
• Figure 6: The centralized, decentralized, and FL-based approaches.