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 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)
• 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)
• EASRec: Elastic Architecture Search for Efficient Long-term Sequential Recommender Systems [82.76483989905961]
  Current Sequential Recommender Systems (SRSs) suffer from computational and resource inefficiencies. We develop the Elastic Architecture Search for Efficient Long-term Sequential Recommender Systems (EASRec) EASRec introduces data-aware gates that leverage historical information from input data batch to improve the performance of the recommendation network.
  arXiv  Detail & Related papers  (2024-02-01T07:22:52Z)
• Embedding in Recommender Systems: A Survey [67.67966158305603]
  A crucial aspect is embedding techniques that covert the high-dimensional discrete features, such as user and item IDs, into low-dimensional continuous vectors. Applying embedding techniques captures complex entity relationships and has spurred substantial research. This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques.
  arXiv  Detail & Related papers  (2023-10-28T06:31:06Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• 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)
• A Survey on Federated Recommendation Systems [40.46436329232597]
  Federated learning has been applied to recommendation systems to protect user privacy. In federated learning settings, recommendation systems can train recommendation models only collecting the intermediate parameters instead of the real user data.
  arXiv  Detail & Related papers  (2022-12-27T08:09:45Z)
• 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.