Federated Recommendation (FR) emerges as a novel paradigm that enables
privacy-preserving recommendations. However, traditional FR systems usually
represent users/items with discrete identities (IDs), suffering from
performance degradation due to the data sparsity and heterogeneity in FR. On
the other hand, Large Language Models (LLMs) as recommenders have proven
effective across various recommendation scenarios. Yet, LLM-based recommenders
encounter challenges such as low inference efficiency and potential
hallucination, compromising their performance in real-world scenarios. To this
end, we propose GPT-FedRec, a federated recommendation framework leveraging
ChatGPT and a novel hybrid Retrieval Augmented Generation (RAG) mechanism.
GPT-FedRec is a two-stage solution. The first stage is a hybrid retrieval
process, mining ID-based user patterns and text-based item features. Next, the
retrieved results are converted into text prompts and fed into GPT for
re-ranking. Our proposed hybrid retrieval mechanism and LLM-based re-rank aims
to extract generalized features from data and exploit pretrained knowledge
within LLM, overcoming data sparsity and heterogeneity in FR. In addition, the
RAG approach also prevents LLM hallucination, improving the recommendation
performance for real-world users. Experimental results on diverse benchmark
datasets demonstrate the superior performance of GPT-FedRec against
state-of-the-art baseline methods.

联邦推荐 （FR）是一种新的范式，它可以实现隐私保护推荐。然而，传统的 FR 系统通常使用离散标识符（IDs）来表示用户 / 物品，由于 FR 中的数据稀疏性和异质性而导致性能下降。相比之下，基于大型语言模型（LLMs）的推荐器已经在各种推荐场景中证明了其有效性。我们提出了 GPT-FedRec，这是一个利用 ChatGPT 和一种新的混合检索增强生成（RAG）机制的联邦推荐框架。GPT-FedRec 是一个两阶段的解决方案。第一阶段是混合检索过程，挖掘基于 ID 的用户模式和基于文本的物品特征。然后，将检索到的结果转换为文本提示，并输入 GPT 进行重新排序。我们提出的混合检索机制和基于 LLM 的重新排序旨在从数据中提取概括特征，并利用 LLM 内部的预训练知识，克服 FR 中的数据稀疏性和异质性。此外，RAG 方法还可以防止 LLM 的产生虚假数据，提高了针对真实用户的推荐性能。对多种基准数据集的实验结果表明，GPT-FedRec 相对于最先进的基线方法具有优越的性能。