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标题: "FedCGR: Federated Cross-Domain Generative Recommendation"
原文链接: "https://arxiv.org/abs/2608.10929"
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## Computer Science > Artificial Intelligence

## Title:FedCGR: Federated Cross-Domain Generative Recommendation

Authors:[Zhuodong Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+Z), [Hugen Lv](https://arxiv.org/search/cs?searchtype=author&query=Lv,+H), [Xiangyu Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+X), [Bohan Guo](https://arxiv.org/search/cs?searchtype=author&query=Guo,+B), [Peiyu Hu](https://arxiv.org/search/cs?searchtype=author&query=Hu,+P)

[View PDF](https://arxiv.org/pdf/2608.10929) [HTML (experimental)](https://arxiv.org/html/2608.10929v1)

> Abstract: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.

| Comments: |  |
| --- | --- |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | [arXiv:2608.10929](https://arxiv.org/abs/2608.10929) \[cs.AI\] |
|  | (or [arXiv:2608.10929v1](https://arxiv.org/abs/2608.10929v1) \[cs.AI\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10929](https://doi.org/10.48550/arXiv.2608.10929) |

## Submission history

From: Zhuodong Liu \[[view email](https://arxiv.org/show-email/e3558bfe/2608.10929)\]  
**\[v1\]** Tue, 11 Aug 2026 13:58:55 UTC (859 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10929) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
