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.

翻译：联邦推荐（FR）作为一种新型范式，能够实现隐私保护的推荐。然而，传统FR系统通常使用离散身份标识（ID）表示用户/项目，受限于数据稀疏性和异质性导致性能下降。另一方面，基于大语言模型（LLM）的推荐器已证明在多种推荐场景中的有效性。但LLM推荐器面临推理效率低下和潜在幻觉问题，影响其在真实场景中的性能。为此，我们提出GPT-FedRec——一种利用ChatGPT和新型混合检索增强生成（RAG）机制的联邦推荐框架。GPT-FedRec采用两阶段解决方案：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项目特征；随后将检索结果转化为文本提示输入GPT进行重排序。我们提出的混合检索机制与基于LLM的重排序方法，旨在从数据中提取通用特征并利用LLM的预训练知识，克服FR中的数据稀疏性和异质性。此外，RAG方法还能防止LLM幻觉，提升真实用户的推荐性能。在多个基准数据集上的实验结果表明，GPT-FedRec相较于最先进的基线方法具有卓越性能。