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

2024-10-03 04:11:42
Qianru Zhang, Peng Yang, Junliang Yu, Haixin Wang, Xingwei He, Siu-Ming Yiu, Hongzhi Yin

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.

Abstract (translated)

智能手机和基于位置的社会网络的广泛采用导致了大量的空间-时间数据的大幅涌入,为增强点 of interest(POI)推荐系统提供了无与伦比的机会。这些先进的POI系统对于丰富用户体验、实现个性化互动和优化数字环境中的决策过程至关重要。然而,现有的调查往往关注传统方法,很少有调查深入探讨前沿发展、新兴架构以及POI推荐中的安全问题。为了填补这一空白,我们的调查在POI推荐系统的全面、最新的回顾中脱颖而出,涵盖了模型、架构和安全方面的进步。我们系统地研究了从传统方法到先进技术的转变,例如大型语言模型。此外,我们探讨了从集中式到去中心化和联邦学习的架构进化,突出了可扩展性和隐私的改进。此外,我们还关注了日益重要的安全性,探讨了潜在的漏洞和隐私保护方法。我们的分类系统为POI推荐当前状态提供了结构化的概述,同时我们还在这个快速发展的领域中发现了有前景的研究方向。

URL

https://arxiv.org/abs/2410.02191

PDF

https://arxiv.org/pdf/2410.02191.pdf


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