With the recent success of large language models, particularly foundation models with generalization abilities, applying foundation models for recommendations becomes a new paradigm to improve existing recommendation systems. It becomes a new open challenge to enable the foundation model to capture user preference changes in a timely manner with reasonable communication and computation costs while preserving privacy. This paper proposes a novel federated adaptation mechanism to enhance the foundation model-based recommendation system in a privacy-preserving manner. Specifically, each client will learn a lightweight personalized adapter using its private data. The adapter then collaborates with pre-trained foundation models to provide recommendation service efficiently with fine-grained manners. Importantly, users' private behavioral data remains secure as it is not shared with the server. This data localization-based privacy preservation is embodied via the federated learning framework. The model can ensure that shared knowledge is incorporated into all adapters while simultaneously preserving each user's personal preferences. Experimental results on four benchmark datasets demonstrate our method's superior performance. Implementation code is available to ease reproducibility.

翻译：随着大语言模型（尤其是具有泛化能力的基础模型）的成功，将基础模型应用于推荐成为改进现有推荐系统的新范式。如何使基础模型在合理通信与计算成本下，及时捕捉用户偏好变化并保护隐私，成为新的开放性挑战。本文提出一种新颖的联邦适配机制，以隐私保护方式增强基于基础模型的推荐系统。具体而言，每个客户端利用其私有数据学习轻量级个性化适配器。该适配器随后与预训练基础模型协同工作，以精细化的方式高效提供推荐服务。重要的是，用户的私有行为数据保持安全，不共享给服务器。这种基于数据本地化的隐私保护通过联邦学习框架实现。模型既能确保共享知识融入所有适配器，又能同时保留每个用户的个性化偏好。在四个基准数据集上的实验结果证明了我们方法的优越性能。代码已开源以促进可复现性。