mlsanityData to 7 Oct 2026

Federated Recommendation via Hybrid Retrieval Augmented Generation

arXiv 2403.04256 ↗First posted 7 Mar 2024cs.IR cs.AIcited by 16

On the map: Retrieval-augmented generation → RAG for recommendation

Abstract (arXiv)

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.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Placed in RAG for recommendation, one of 15 directions in Retrieval-augmented generation. Builds on 34 of its 43 references that are in the map. First posted on arXiv 7 Mar 2024.

Builds on

34 of 43 references.

References · 34

  1. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
  4. 2018Self-Attentive Sequential Recommendationcited by 1,169
  5. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  6. 2019Graph Neural Networks for Social Recommendationcited by 711
  7. 2015Image-based Recommendations on Styles and Substitutescited by 702
  8. 2016Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filteringcited by 517
  9. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 396
  10. 2023Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agentscited by 356
  11. 2023TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationcited by 336
  12. 2020Autoregressive Entity Retrievalcited by 333
22 more
  1. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 307
  2. 2024MoE-LLaVA: Mixture of Experts for Large Vision-Language Modelscited by 298
  3. 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 266
  4. 2022Transformer Memory as a Differentiable Search Indexcited by 242
  5. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  6. 2023Text Is All You Need: Learning Language Representations for Sequential Recommendationcited by 149
  7. 2023Large Language Models as Zero-Shot Conversational Recommenderscited by 126
  8. 2022Towards Universal Sequence Representation Learning for Recommender Systemscited by 120
  9. 2023Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Predictioncited by 113
  10. 2022Learning Vector-Quantized Item Representation for Transferable Sequential Recommenderscited by 112
  11. 2019Secure Federated Matrix Factorizationcited by 102
  12. 2023PALR: Personalization Aware LLMs for Recommendationcited by 96
  13. 2019Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation Systemcited by 79
  14. 2023Leveraging Large Language Models for Sequential Recommendationcited by 78
  15. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 72
  16. 2023LlamaRec: Two-Stage Recommendation using Large Language Models for Rankingcited by 52
  17. 2023Linear Recurrent Units for Sequential Recommendationcited by 32
  18. 2021Zero-Shot Recommendation as Language Modelingcited by 28
  19. 2023BookGPT: A General Framework for Book Recommendation Empowered by Large Language Modelcited by 22
  20. 2022Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenderscited by 11
  21. 2022Towards Communication Efficient and Fair Federated Personalized Sequential Recommendationcited by 7
  22. 2022FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendationcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 6; 2026: 3.

16 papers.

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By area:

  1. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 170
  2. 2024Advances and Open Challenges in Federated Foundation Modelscited by 33
  3. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  4. 2024Multi-Head RAG: Solving Multi-Aspect Problems with LLMscited by 18
  5. 2025Toward Agentic AI: Generative Information Retrieval Inspired Intelligent Communications and Networkingcited by 9
  6. 2025Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Basescited by 9
  7. 2025RALLRec+: Retrieval Augmented Large Language Model Recommendation with Reasoningcited by 5
  8. 2025A Systematic Survey on Federated Sequential Recommendationcited by 4
  9. 2025RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendationcited by 3
  10. 2026Autonomous Information Seeking: A Roadmap for Agentic Recommender Systemscited by 3
  11. 2024Privacy in LLM-based Recommendation: Recent Advances and Future Directionscited by 2
  12. 2024Transferable Sequential Recommendation via Vector Quantized Meta Learningcited by 0
4 more
  1. 2024Dehallucinating Parallel Context Extension for Retrieval-Augmented Generationcited by 0
  2. 2025CLLMRec: LLM-powered Cognitive-Aware Concept Recommendation via Semantic Alignment and Prerequisite Knowledge Distillationcited by 0
  3. 2026Synapse: Federated Tool Routing via Typed Compendium Artifactscited by 0
  4. 2026Improving Federated Graph Recommendation with Semantic Guidancecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026FeDecider: An LLM-Based Framework for Federated Cross-Domain Recommendation ↗
  2. 2025Federated Retrieval-Augmented Generation: A Systematic Mapping Study
  3. 2025WebRec: Enhancing LLM-based Recommendations with Attention-guided RAG from Web
  4. 2025Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation
  5. 2026FedMosaic: Federated Retrieval-Augmented Generation via Parametric Adapters
  6. 2025MDiffFR: Modality-Guided Diffusion Generation for Cold-start Items in Federated Recommendation
  7. 2024IDGenRec: LLM-RecSys Alignment with Textual ID Learning
  8. 2025Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation