A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security
- URL: http://arxiv.org/abs/2410.02191v1
- Date: Thu, 3 Oct 2024 04:11:42 GMT
- Title: A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security
- Authors: Qianru Zhang, Peng Yang, Junliang Yu, Haixin Wang, Xingwei He, Siu-Ming Yiu, Hongzhi Yin,
- Abstract summary: Point-of-Interest (POI) recommendation systems are crucial for enriching user experiences, enabling personalized interactions, and optimizing decision-making processes in the digital landscape.
We systematically examine the transition from traditional models to advanced techniques such as large language models.
We address the increasing importance of security, examining potential vulnerabilities and privacy-preserving approaches.
- Score: 40.18083295666298
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- A Survey of Model Architectures in Information Retrieval [64.75808744228067]
We focus on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation.
We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs)
We conclude by discussing emerging challenges and future directions, including architectural optimizations for performance and scalability, handling of multimodal, multilingual data, and adaptation to novel application domains beyond traditional search paradigms.
arXiv Detail & Related papers (2025-02-20T18:42:58Z) - Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation [85.52251362906418]
This tutorial explores two primary approaches for integrating large language models (LLMs)
It provides a comprehensive overview of generative large recommendation models, including their recent advancements, challenges, and potential research directions.
Key topics include data quality, scaling laws, user behavior mining, and efficiency in training and inference.
arXiv Detail & Related papers (2025-02-19T14:48:25Z) - Graph Foundation Models for Recommendation: A Comprehensive Survey [55.70529188101446]
Large language models (LLMs) are designed to process and comprehend natural language, making both approaches highly effective and widely adopted.
Recent research has focused on graph foundation models (GFMs)
GFMs integrate the strengths of GNNs and LLMs to model complex RS problems more efficiently by leveraging the graph-based structure of user-item relationships alongside textual understanding.
arXiv Detail & Related papers (2025-02-12T12:13:51Z) - A survey on secure decentralized optimization and learning [5.794084857284833]
Decentralized optimization has become a standard paradigm for solving large-scale decision-making problems without centralizing data.
This paradigm introduces new privacy and security risks, with malicious agents potentially able to infer private data or impair the model accuracy.
This survey provides a comprehensive tutorial on these advancements.
arXiv Detail & Related papers (2024-08-16T09:42:19Z) - A Survey on Intent-aware Recommender Systems [8.761638205244427]
A recommender system should aim to take the users' probable intent of using the service at a certain point in time into account.
In this paper, we survey and categorize existing approaches to building the next generation of Intent-Aware Recommender Systems.
arXiv Detail & Related papers (2024-06-24T06:46:32Z) - Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations [39.29562719543681]
Next Point-of-Interest (POI) recommendations leverage historical check-in data to predict users' next POIs to visit.
Traditional centralized deep neural networks (DNNs) offer impressive POI recommendation performance but face challenges due to privacy concerns and limited timeliness.
On-device POI recommendations have been introduced, utilizing federated learning (FL) and decentralized approaches to ensure privacy and recommendation timeliness.
This paper introduces a novel collaborative learning framework, Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations (DCPR)
arXiv Detail & Related papers (2024-05-22T16:41:23Z) - Emerging Synergies Between Large Language Models and Machine Learning in
Ecommerce Recommendations [19.405233437533713]
Large language models (LLMs) have superior capabilities in basic tasks of language understanding and generation.
We introduce a representative approach to learning user and item representations using LLM as a feature encoder.
We then reviewed the latest advances in LLMs techniques for collaborative filtering enhanced recommendation systems.
arXiv Detail & Related papers (2024-03-05T08:31:00Z) - Recommender Systems in the Era of Large Language Models (LLMs) [62.0129013439038]
Large Language Models (LLMs) have revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI)
We conduct a comprehensive review of LLM-empowered recommender systems from various aspects including Pre-training, Fine-tuning, and Prompting.
arXiv Detail & Related papers (2023-07-05T06:03:40Z) - Self-supervised Graph-based Point-of-interest Recommendation [66.58064122520747]
Next Point-of-Interest (POI) recommendation has become a prominent component in location-based e-commerce.
We propose a Self-supervised Graph-enhanced POI Recommender (S2GRec) for next POI recommendation.
In particular, we devise a novel Graph-enhanced Self-attentive layer to incorporate the collaborative signals from both global transition graph and local trajectory graphs.
arXiv Detail & Related papers (2022-10-22T17:29:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.