On-Device Recommender Systems: A Comprehensive Survey

• URL: http://arxiv.org/abs/2401.11441v3
• Date: Wed, 06 Aug 2025 03:23:30 GMT
• Title: On-Device Recommender Systems: A Comprehensive Survey
• Authors: Hongzhi Yin, Liang Qu, Tong Chen, Wei Yuan, Ruiqi Zheng, Jing Long, Xin Xia, Yuhui Shi, Chengqi Zhang,
• Abstract summary: Traditional recommender systems suffer from excessive resource consumption, response latency, as well as privacy and security risks.<n>On-device recommender systems (DeviceRSs) leverage the capabilities of edge devices to minimize centralized data storage requirements.<n>Despite the rapid rise of DeviceRSs, there is a clear absence of timely literature reviews that systematically introduce, categorize and contrast these methods.
• Score: 45.631205573270805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender systems have been widely deployed in various real-world applications to help users identify content of interest from massive amounts of information. Traditional recommender systems work by collecting user-item interaction data in a cloud-based data center and training a centralized model to perform the recommendation service. However, such cloud-based recommender systems (CloudRSs) inevitably suffer from excessive resource consumption, response latency, as well as privacy and security risks concerning both data and models. Recently, driven by the advances in storage, communication, and computation capabilities of edge devices, there has been a shift of focus from CloudRSs to on-device recommender systems (DeviceRSs), which leverage the capabilities of edge devices to minimize centralized data storage requirements, reduce the response latency caused by communication overheads, and enhance user privacy and security by localizing data processing and model training. Despite the rapid rise of DeviceRSs, there is a clear absence of timely literature reviews that systematically introduce, categorize and contrast these methods. To bridge this gap, we aim to provide a comprehensive survey of DeviceRSs, covering three main aspects: (1) the deployment and inference of DeviceRSs (2) the training and update of DeviceRSs (3) the security and privacy of DeviceRSs. Furthermore, we provide a fine-grained and systematic taxonomy of the methods involved in each aspect, followed by a discussion regarding challenges and future research directions. This is the first comprehensive survey on DeviceRSs that covers a spectrum of tasks to fit various needs. We believe this survey will help readers effectively grasp the current research status in this field, equip them with relevant technical foundations, and stimulate new research ideas for developing DeviceRSs.

Related papers

• Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications [0.15705429611931052]
  Small object detection (SOD) is a critical yet challenging task in computer vision.<n>Recent advancements in deep learning have introduced innovative solutions.<n>Emerging trends such as lightweight neural networks, knowledge distillation (KD), and self-supervised learning offer promising directions for improving detection efficiency.
  arXiv  Detail & Related papers  (2025-03-26T12:58:13Z)
• A Tutorial of Personalized Federated Recommender Systems: Recent Advances and Future Directions [19.74113941915374]
  Personalization stands as the cornerstone of recommender systems (RecSys)<n>FedRecSys enable users to retain personal data locally and solely share model parameters with low privacy sensitivity for global model training.<n>This tutorial seeks to provide an introduction to PFedRecSys, encompassing (1) an overview of existing studies on PFedRecSys, (2) a comprehensive taxonomy of PFedRecSys, and (3) exploration of open challenges and promising future directions in PFedRecSys.
  arXiv  Detail & Related papers  (2024-12-11T03:33:51Z)
• RAGGED: Towards Informed Design of Scalable and Stable RAG Systems [51.171355532527365]
  Retrieval-augmented generation (RAG) enhances language models by integrating external knowledge.<n>RAGGED is a framework for systematically evaluating RAG systems.
  arXiv  Detail & Related papers  (2024-03-14T02:26:31Z)
• DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems [79.76519917171261]
  This paper addresses the computational overhead and resource inefficiency prevalent in Sequential Recommender Systems (SRSs)<n>We introduce an innovative approach combining pruning methods with advanced model designs.<n>Our principal contribution is the development of a Data-aware Neural Architecture Search for Recommender System (DNS-Rec)
  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)
• Robust Recommender System: A Survey and Future Directions [58.87305602959857]
  We first present a taxonomy to organize current techniques for withstanding malicious attacks and natural noise.<n>We then explore state-of-the-art methods in each category, including fraudster detection, adversarial training, certifiable robust training for defending against malicious attacks.<n>We discuss robustness across varying recommendation scenarios and its interplay with other properties like accuracy, interpretability, privacy, and fairness.
  arXiv  Detail & Related papers  (2023-09-05T08:58:46Z)
• 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)
• Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions [0.20236506875465865]
  Intrusion detection systems play a significant role in ensuring security and privacy of smart devices. The present paper aims to present an extensive and exhaustive review on the use of FL in intrusion detection system.
  arXiv  Detail & Related papers  (2021-06-16T13:13:04Z)
• Towards AIOps in Edge Computing Environments [60.27785717687999]
  This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments. It is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices.
  arXiv  Detail & Related papers  (2021-02-12T09:33:00Z)
• Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective [27.11589218811911]
  generative adversarial networks (GANs) have garnered increased interest in many fields, owing to their strong capacity to learn complex real data distributions. In this paper, we propose a taxonomy of these models, along with their detailed descriptions and advantages.
  arXiv  Detail & Related papers  (2020-03-05T08:05:38Z)

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.