As user behavior data becomes increasingly scattered across different platforms, achieving cross-domain knowledge fusion while preserving privacy has become a critical issue in recommender systems. Existing PPCDR methods usually rely on overlapping users or items as a bridge, making them inapplicable to non-overlapping scenarios. They also suffer from limitations in the collaborative modeling of global and local semantics. To this end, this paper proposes a Federated Cross-domain Recommendation method with deep knowledge Fusion (FedCRF). Using textual semantics as a cross-domain bridge, FedCRF achieves cross-domain knowledge transfer via federated semantic learning under the non-overlapping scenario. Specifically, FedCRF constructs global semantic clusters on the server side to extract shared semantic information, and designs a FGSAT module on the client side to dynamically adapt to local data distributions and alleviate cross-domain distribution shift. Meanwhile, it builds a semantic graph based on textual features to learn representations that integrate both structural and semantic information, and introduces contrastive learning constraints between global and local semantic representations to enhance semantic consistency and promote deep knowledge fusion. In this framework, only item semantic representations are shared, while user interaction data remains locally stored, effectively mitigating privacy leakage risks. Experimental results on multiple real-world datasets show that FedCRF significantly outperforms existing methods in terms of Recall@20 and NDCG@20, validating its effectiveness and superiority in non-overlapping cross-domain recommendation scenarios.

翻译：随着用户行为数据在不同平台间日益分散，如何在保护隐私的前提下实现跨域知识融合已成为推荐系统中的关键问题。现有隐私保护跨域推荐方法通常依赖重叠用户或物品作为桥梁，导致其无法适用于无重叠场景，同时在全球与局部语义的协同建模方面存在局限。为此，本文提出一种融合深度知识的分层跨域推荐方法（FedCRF）。该方法以文本语义作为跨域桥梁，通过分层语义学习在无重叠场景下实现跨域知识迁移。具体而言，FedCRF在服务器端构建全局语义簇以提取共享语义信息，并在客户端设计FGSAT模块以动态适应当地数据分布、缓解跨域分布偏移。同时，基于文本特征构建语义图，学习融合结构与语义信息的表征，并引入全局与局部语义表征间的对比学习约束以增强语义一致性、促进深度知识融合。在该框架中，仅共享物品语义表征，用户交互数据则本地存储，有效降低了隐私泄露风险。在多个真实数据集上的实验结果表明，FedCRF在Recall@20和NDCG@20指标上显著优于现有方法，验证了其在无重叠跨域推荐场景中的有效性与优越性。