Given the sheer volume of contemporary e-commerce applications, recommender systems (RSs) have gained significant attention in both academia and industry. However, traditional cloud-based RSs face inevitable challenges, such as resource-intensive computation, reliance on network access, and privacy breaches. In response, a new paradigm called on-device recommender systems (ODRSs) has emerged recently in various industries like Taobao, Google, and Kuaishou. ODRSs unleash the computational capacity of user devices with lightweight recommendation models tailored for resource-constrained environments, enabling real-time inference with users' local data. This tutorial aims to systematically introduce methodologies of ODRSs, including (1) an overview of existing research on ODRSs; (2) a comprehensive taxonomy of ODRSs, where the core technical content to be covered span across three major ODRS research directions, including on-device deployment and inference, on-device training, and privacy/security of ODRSs; (3) limitations and future directions of ODRSs. This tutorial expects to lay the foundation and spark new insights for follow-up research and applications concerning this new recommendation paradigm.

翻译：鉴于当代电子商务应用的庞大规模，推荐系统在学术界和工业界均获得了显著关注。然而，传统基于云的推荐系统面临不可避免的挑战，例如资源密集型计算、对网络访问的依赖以及隐私泄露。作为回应，一种名为设备端推荐系统的新范式近期已在淘宝、Google和快手等多个行业中出现。设备端推荐系统通过为资源受限环境量身定制的轻量级推荐模型释放用户设备的计算能力，从而能够利用用户的本地数据进行实时推理。本教程旨在系统性地介绍设备端推荐系统的方法论，内容包括：(1)设备端推荐系统现有研究概述；(2)设备端推荐系统的全面分类体系，涵盖三大核心研究方向：设备端部署与推理、设备端训练，以及设备端推荐的隐私与安全性；(3)设备端推荐系统的局限性及未来发展方向。本教程期望为这种新推荐范式的后续研究与应用奠定基础并激发新思路。