Personalization stands as the cornerstone of recommender systems (RecSys), striving to sift out redundant information and offer tailor-made services for users. However, the conventional cloud-based RecSys necessitates centralized data collection, posing significant risks of user privacy breaches. In response to this challenge, federated recommender systems (FedRecSys) have emerged, garnering considerable attention. FedRecSys enable users to retain personal data locally and solely share model parameters with low privacy sensitivity for global model training, significantly bolstering the system's privacy protection capabilities. Within the distributed learning framework, the pronounced non-iid nature of user behavior data introduces fresh hurdles to federated optimization. Meanwhile, the ability of federated learning to concurrently learn multiple models presents an opportunity for personalized user modeling. Consequently, the development of personalized FedRecSys (PFedRecSys) is crucial and holds substantial significance. This tutorial seeks to provide an introduction to PFedRecSys, encompassing (1) an overview of existing studies on PFedRecSys, (2) a comprehensive taxonomy of PFedRecSys spanning four pivotal research directions-client-side adaptation, server-side aggregation, communication efficiency, privacy and protection, and (3) exploration of open challenges and promising future directions in PFedRecSys. This tutorial aims to establish a robust foundation and spark new perspectives for subsequent exploration and practical implementations in the evolving realm of RecSys.

翻译：个性化是推荐系统（RecSys）的基石，旨在筛选冗余信息并为用户提供定制化服务。然而，传统的基于云端的推荐系统需要集中收集数据，存在用户隐私泄露的重大风险。为应对这一挑战，联邦推荐系统（FedRecSys）应运而生并受到广泛关注。FedRecSys允许用户将个人数据保留在本地，仅共享隐私敏感性较低的模型参数用于全局模型训练，从而显著增强了系统的隐私保护能力。在分布式学习框架下，用户行为数据显著的非独立同分布特性给联邦优化带来了新的挑战。同时，联邦学习能够并行学习多个模型，这为个性化用户建模提供了契机。因此，发展个性化联邦推荐系统（PFedRecSys）至关重要且意义重大。本教程旨在介绍PFedRecSys，内容包括：（1）现有PFedRecSys研究的概述；（2）涵盖四个关键研究方向——客户端适配、服务器端聚合、通信效率、隐私与保护——的PFedRecSys全面分类体系；（3）探讨PFedRecSys领域面临的开放性挑战与有前景的未来方向。本教程旨在为推荐系统这一动态发展领域的后续探索与实践应用奠定坚实基础并激发新视角。