联邦学习下用原型对比学习实现跨域推荐，保护隐私且无需重叠用户。
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain Recommendation
- 基于联邦学习与原型对比，利用非重叠用户信息提升推荐效果。
- 在亚马逊和豆瓣数据集上优于现有最优方法，跨域推荐准确率显著提升。
- 适合注重用户隐私、缺乏重叠用户的跨域推荐场景使用。
跨域推荐（CDR）旨在通过从数据丰富的域迁移知识来提升稀疏域的推荐准确性。然而，现有方法通常假设跨域的用户-物品交互数据公开可得，忽视了用户隐私问题。此外，由于依赖大量完全重叠用户进行知识迁移，当重叠用户稀少时性能显著下降。为此，我们提出一种面向隐私保护的联邦原型对比学习框架 FedPCL-CDR。该框架在联邦学习架构下，利用非重叠用户信息与差分原型，提升模型性能。FedPCL-CDR 包含两个关键模块：本地域（客户端）学习与全局服务器聚合。本地域中，首先对用户数据聚类，并结合本地差分隐私（LDP）学习差分原型，有效利用非重叠用户信息并保护隐私；随后通过对比学习方式，使用来自服务器返回的本地与全局原型进行知识迁移。服务器端则聚合各客户端发送的差分原型，学习本地与全局原型。在四个 CDR 任务上的广泛实验表明，FedPCL-CDR 在 Amazon 与 Douban 数据集上均超越现有最先进基线。代码已开源：https://github.com/Lili1013/FedPCL-CDR。
原文摘要 · Abstract (English)
Cross-domain recommendation (CDR) aims to improve recommendation accuracy in sparse domains by transferring knowledge from data-rich domains. However, existing CDR approaches often assume that user-item interaction data across domains is publicly available, neglecting user privacy concerns. Additionally, they experience performance degradation with sparse overlapping users due to their reliance on a large number of fully shared users for knowledge transfer. To address these challenges, we propose a Federated Prototype-based Contrastive Learning (CL) framework for Privacy Preserving CDR, called FedPCL-CDR. This approach utilizes non-overlapping user information and differential prototypes to improve model performance within a federated learning framework. FedPCL-CDR comprises two key modules: local domain (client) learning and global server aggregation. In the local domain, FedPCL-CDR first clusters all user data and utilizes local differential privacy (LDP) to learn differential prototypes, effectively utilizing non-overlapping user information and protecting user privacy. It then conducts knowledge transfer by employing both local and global prototypes returned from the server in a CL manner. Meanwhile, the global server aggregates differential prototypes sent from local domains to learn both local and global prototypes. Extensive experiments on four CDR tasks across Amazon and Douban datasets demonstrate that FedPCL-CDR surpasses SOTA baselines. We release our code at https://github.com/Lili1013/FedPCL CDR
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。