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arXiv 2405.04840cs.IR
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基于联邦自适应的基础模型推荐

Federated Adaptation for Foundation Model-based Recommendations

Chunxu Zhang, Guodong Long, Hongkuan Guo, Xiao Fang, Yang Song, Zhaojie Liu, Guorui Zhou, Zijian Zhang, Yang Liu, Bo Yang

备注：
Accepted as a regular paper of IJCAI'24
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AI总结：

针对基础模型推荐中隐私保护与实时偏好适应问题，提出联邦学习轻量个性化适配器，在不共享用户私有数据的前提下融合共享知识并保留个人偏好，且在四个基准上性能优越。

摘要：

随着大语言模型，尤其是具备泛化能力的基础模型近期取得成功，将基础模型应用于推荐成为改进现有推荐系统的一种新范式。如何在保护隐私的同时，以合理的通信与计算成本使基础模型及时捕捉用户偏好变化，成为一个新的开放性挑战。本文提出一种新颖的联邦自适应机制，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一个轻量级个性化适配器。随后，该适配器与预训练基础模型协作，以细粒度方式高效提供推荐服务。重要的是，用户的私有行为数据不会与服务器共享，因此保持安全。这种基于数据本地化的隐私保护通过联邦学习框架得以体现。该模型能够确保共享知识被融入所有适配器，同时保留每个用户的个人偏好。在四个基准数据集上的实验结果证明了本文方法的优越性能。为便于复现，实现代码已公开。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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