The paper "Federated Recommendation via Hybrid Retrieval Augmented Generation" addresses the challenges of data sparsity and heterogeneity in federated recommendation (FR) systems, which often suffer from performance degradation due to discrete user/item representations. To overcome these issues, the authors propose GPT-FedRec, a two-stage framework that leverages ChatGPT and a hybrid Retrieval Augmented Generation (RAG) mechanism. The first stage involves a hybrid retrieval process that combines ID-based and text-based retrieval to mine user patterns and item features. The retrieved results are then fed into GPT for re-ranking, enhancing the generalization and accuracy of recommendations. This approach not only improves the performance in data-sparse and heterogeneous FR settings but also prevents hallucination, a common issue with LLM-based recommenders. Experimental results on various benchmark datasets demonstrate the superior performance of GPT-FedRec compared to state-of-the-art baseline methods. The paper also includes a detailed evaluation of the framework's effectiveness, sensitivity analysis, and ablation studies, highlighting the key contributions and limitations of the proposed method.The paper "Federated Recommendation via Hybrid Retrieval Augmented Generation" addresses the challenges of data sparsity and heterogeneity in federated recommendation (FR) systems, which often suffer from performance degradation due to discrete user/item representations. To overcome these issues, the authors propose GPT-FedRec, a two-stage framework that leverages ChatGPT and a hybrid Retrieval Augmented Generation (RAG) mechanism. The first stage involves a hybrid retrieval process that combines ID-based and text-based retrieval to mine user patterns and item features. The retrieved results are then fed into GPT for re-ranking, enhancing the generalization and accuracy of recommendations. This approach not only improves the performance in data-sparse and heterogeneous FR settings but also prevents hallucination, a common issue with LLM-based recommenders. Experimental results on various benchmark datasets demonstrate the superior performance of GPT-FedRec compared to state-of-the-art baseline methods. The paper also includes a detailed evaluation of the framework's effectiveness, sensitivity analysis, and ablation studies, highlighting the key contributions and limitations of the proposed method.