Cross-domain Recommendation (CDR) as one of the effective techniques in alleviating the data sparsity issues has been widely studied in recent years. However, previous works may cause domain privacy leakage since they necessitate the aggregation of diverse domain data into a centralized server during the training process. Though several studies have conducted privacy preserving CDR via Federated Learning (FL), they still have the following limitations: 1) They need to upload users' personal information to the central server, posing the risk of leaking user privacy. 2) Existing federated methods mainly rely on atomic item IDs to represent items, which prevents them from modeling items in a unified feature space, increasing the challenge of knowledge transfer among domains. 3) They are all based on the premise of knowing overlapped users between domains, which proves impractical in real-world applications. To address the above limitations, we focus on Privacy-preserving Cross-domain Recommendation (PCDR) and propose PFCR as our solution. For Limitation 1, we develop a FL schema by exclusively utilizing users' interactions with local clients and devising an encryption method for gradient encryption. For Limitation 2, we model items in a universal feature space by their description texts. For Limitation 3, we initially learn federated content representations, harnessing the generality of natural language to establish bridges between domains. Subsequently, we craft two prompt fine-tuning strategies to tailor the pre-trained model to the target domain. Extensive experiments on two real-world datasets demonstrate the superiority of our PFCR method compared to the SOTA approaches.

翻译：跨域推荐（CDR）作为缓解数据稀疏问题的有效技术之一，近年来得到了广泛研究。然而，以往的工作可能导致域隐私泄露，因为它们在训练过程中需要将不同域的数据聚合到中央服务器。尽管有几项研究通过联邦学习（FL）实现了隐私保护的CDR，但它们仍然存在以下局限性：1）需要将用户的个人信息上传到中央服务器，存在用户隐私泄露的风险。2）现有的联邦方法主要依赖原子项目ID来表示项目，这使其无法在统一特征空间中对项目进行建模，增加了域间知识迁移的挑战。3）它们都基于已知域间重叠用户的前提，这在现实应用中不切实际。为解决上述局限性，我们专注于隐私保护跨域推荐（PCDR），并提出PFCR作为解决方案。针对局限性1，我们开发了一种联邦学习架构，仅利用用户与本地客户端的交互，并设计了一种梯度加密方法。针对局限性2，我们通过项目的描述文本在通用特征空间中对其进行建模。针对局限性3，我们首先学习联邦内容表示，利用自然语言的通用性建立域间桥梁。随后，我们设计了两种提示微调策略，将预训练模型适配到目标域。在两个真实数据集上的大量实验表明，我们的PFCR方法相比现有最优方法具有优越性。