  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.
