发表机构
Beijing Jiaotong University; Shanghai Jiao Tong University; University of Malaya（北京交通大学; 上海交通大学; 马来亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对联邦跨域推荐的物品空间对齐难题，提出FedCGR框架，通过稳定语义物品语言与可靠性感知接口注入局部协同过滤证据，在六个亚马逊跨域场景中性能优于基线。
AI 中文摘要
跨域推荐（CDR）在相关域间迁移偏好知识，但联邦部署使跨域对齐变得困难，因为用于对齐物品空间的行为锚点（如重叠用户和共享交互信号）在各客户端间往往稀疏、不可用或具有隐私敏感性。为解决这一矛盾，我们将联邦CDR重新定义为基于稳定语义物品语言的生成任务。通过将物品表示为源自公开物品侧元数据的离散语义ID（SID）序列，跨域物品对齐由共享词汇表诱导，而非通过交换私有交互或对齐特定域的嵌入。然而，直接联邦化基于SID的生成器会引入两个设计约束：SID分词器必须保持固定以维持客户端间的token一致性，这造成仅基于语义的瓶颈，因为局部协同过滤（CF）信号无法全局共享或对齐；同时，标准联邦平均会在域异质性下导致负迁移。为克服这些约束，我们提出FedCGR，一种保持物品语言稳定且显式适配的联邦生成式CDR框架。FedCGR通过可靠性感知的语义接口注入局部CF证据，并训练原型个性化生成器，该生成器根据域相关性选择性聚合共享参数，同时保留特定域的量值为局部。在六个亚马逊跨域场景上的实验表明，FedCGR在全排序和抽样评估协议下，始终优于联邦生成式基线，且与强序列式和联邦CDR方法的性能相当。
英文摘要
Cross-domain recommendation (CDR) transfers preference knowledge across related domains, but federated deployment makes cross-domain alignment difficult because the behavioral anchors that align item spaces, such as overlapping users and shared interaction signals, are often sparse, unavailable, or privacy-sensitive across clients. To address this tension, we revisit federated CDR as generation over a stable semantic item language. By representing items as discrete semantic ID (SID) sequences derived from public item-side metadata, cross-domain item alignment is induced by a shared vocabulary rather than by exchanging private interactions or aligning domain-specific embeddings. Directly federating SID-based generators, however, introduces two design constraints: the SID tokenizer must remain fixed to preserve cross-client token consistency, which creates a semantic-only bottleneck because local collaborative filtering (CF) signals cannot be globally shared or aligned; meanwhile, standard federated averaging can cause negative transfer under domain heterogeneity. To overcome these constraints, we propose FedCGR, a federated generative CDR framework that keeps the item language stable and makes adaptation explicit. FedCGR injects local CF evidence through a reliability-aware semantic interface and trains a prototype-personalized generator that selectively aggregates shared parameters according to domain relatedness while keeping domain-specific quantities local. Experiments on six Amazon cross-domain scenarios show that FedCGR consistently outperforms federated generative baselines and achieves competitive performance against strong sequential and federated CDR methods under both full-ranking and sampled evaluation protocols.
CommentsAccepted at CIKM 2026. 10 pages, 5 figures, 6 tables
