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.

翻译：推荐系统已广泛部署于各类实际应用中，帮助用户从海量信息中识别感兴趣的内容。传统推荐系统通过云端数据中心收集用户-物品交互数据，并训练集中式模型来执行推荐服务。然而，这种基于云的推荐系统（CloudRS）不可避免地存在资源消耗过大、响应延迟，以及涉及数据和模型的隐私与安全风险。近年来，随着边缘设备在存储、通信和计算能力上的进步，研究重点已从云端推荐系统转向面向设备的推荐系统（DeviceRS），后者利用边缘设备的能力，最大程度减少集中式数据存储需求，降低通信开销导致的响应延迟，并通过本地化数据处理和模型训练来增强用户隐私与安全性。尽管DeviceRS发展迅速，但目前明显缺乏及时、系统性地介绍、分类和对比这些方法的文献综述。为弥补这一空白，我们旨在对DeviceRS进行全面综述，涵盖三个主要方面：（1）DeviceRS的部署与推理；（2）DeviceRS的训练与更新；（3）DeviceRS的安全与隐私。此外，我们对每个方面涉及的方法进行了细粒度且系统性的分类，并讨论了相关挑战与未来研究方向。这是首个涵盖多种任务以适配不同需求的DeviceRS全面综述。我们相信，本综述将帮助读者有效掌握该领域的研究现状、提供相关的技术基础，并激发开发DeviceRS的新研究思路。