FedCGR: Federated Cross-Domain Generative Recommendation
This work addresses the problem of privacy-preserving cross-domain recommendation for federated learning practitioners, particularly when direct alignment signals are sparse or unavailable. It offers an incremental improvement over existing federated generative methods.
This paper tackles the challenge of cross-domain recommendation in federated settings where user interaction data is private. It proposes FedCGR, a framework that uses a stable semantic item language (SID sequences) to align item spaces and injects local collaborative filtering evidence through a reliability-aware semantic interface. FedCGR consistently outperforms federated generative baselines and achieves competitive performance against strong sequential and federated CDR methods across six Amazon cross-domain scenarios.
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.