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

2024-10-03

arXiv.org

AI Summary

Existing POI recommendation surveys predominantly focus on traditional methods, lacking systematic coverage of emerging topics such as large language models (LLMs), decentralized architectures, and security-privacy trade-offs. To address this gap, we propose the first unified taxonomy integrating *models*, *architectures*, and *security* dimensions, comprehensively surveying advances from 2020 to 2024. Our review encompasses spatiotemporal graph neural networks, LLM-augmented recommendation, paradigm shifts toward federated learning, and applications of differential privacy and adversarial robustness. We provide the first systematic analysis of LLMs and federated learning in POI recommendation—assessing their potential and inherent risks—and identify critical bottlenecks, including dynamic preference modeling and cross-domain privacy-utility trade-offs. Finally, we introduce a theoretical framework and practical roadmap for next-generation POI recommenders that are secure, scalable, and highly personalized.

Technology Category

• Machine Learning: Privacy
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Security and Privacy: Security and privacy of machine learning and AI applications

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.

Problem

• Addresses gaps in existing POI recommendation system surveys.
• Explores advancements in models, architectures, and security.
• Identifies future research directions in POI recommendation systems.

Innovation

• Transition to large language models
• Shift to decentralized, federated architectures
• Focus on security and privacy enhancements