# G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation

arXiv: https://arxiv.org/abs/2502.12586 · HTML: https://mlsanity.com/paper/2502.12586/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Placed in RAG for recommendation, one of 15 directions in Retrieval-augmented generation.
- Builds on 58 of its 91 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Placed in

- Retrieval-augmented generation / [RAG for recommendation](https://mlsanity.com/rag/rag-for-recommendation/)

## Builds on (most cited first, 30 of 58)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [Large Dual Encoders Are Generalizable Retrievers](https://mlsanity.com/paper/2112.07899/) (2021)
- [A Survey on Large Language Models for Recommendation](https://mlsanity.com/paper/2305.19860/) (2023)
- [Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning](https://mlsanity.com/paper/2310.01061/) (2023)
- [Graph Retrieval-Augmented Generation: A Survey](https://mlsanity.com/paper/2408.08921/) (2024)
- [RAFT: Adapting Language Model to Domain Specific RAG](https://mlsanity.com/paper/2403.10131/) (2024)
- [Can Language Models Solve Graph Problems in Natural Language?](https://mlsanity.com/paper/2305.10037/) (2023)
- [One for All: Towards Training One Graph Model for All Classification Tasks](https://mlsanity.com/paper/2310.00149/) (2023)
- [Language is All a Graph Needs](https://mlsanity.com/paper/2308.07134/) (2023)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [RA-DIT: Retrieval-Augmented Dual Instruction Tuning](https://mlsanity.com/paper/2310.01352/) (2023)
- [GraphText: Graph Reasoning in Text Space](https://mlsanity.com/paper/2310.01089/) (2023)
- [Reinforcement Knowledge Graph Reasoning for Explainable Recommendation](https://mlsanity.com/paper/1906.05237/) (2019)
- [Implicit Graph Neural Networks](https://mlsanity.com/paper/2009.06211/) (2020)
- [Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences](https://mlsanity.com/paper/1902.06236/) (2019)

## Built on by (most cited first, 27 of 27)

- [A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment](https://mlsanity.com/paper/2504.15585/) (2025)
- [ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation](https://mlsanity.com/paper/2506.21931/) (2025)
- [GraphRAG-R1: Graph Retrieval-Augmented Generation with Process-Constrained Reinforcement Learning](https://mlsanity.com/paper/2507.23581/) (2025)
- [Rewarding Graph Reasoning Process makes LLMs more Generalized Reasoners](https://mlsanity.com/paper/2503.00845/) (2025)
- [MemWeaver: A Hierarchical Memory from Textual Interactive Behaviors for Personalized Generation](https://mlsanity.com/paper/2510.07713/) (2025)
- [Retrieval-Augmented Purifier for Robust LLM-Empowered Recommendation](https://mlsanity.com/paper/2504.02458/) (2025)
- [From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context](https://mlsanity.com/paper/2508.07117/) (2025)
- [How does Misinformation Affect Large Language Model Behaviors and Preferences?](https://mlsanity.com/paper/2505.21608/) (2025)
- [Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE](https://mlsanity.com/paper/2509.03626/) (2025)
- [Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems](https://mlsanity.com/paper/2607.04433/) (2026)
- [IBCircuit: Towards Holistic Circuit Discovery with Information Bottleneck](https://mlsanity.com/paper/2602.22581/) (2026)
- [InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization](https://mlsanity.com/paper/2503.01488/) (2025)
- [Revisiting LoRA through the Lens of Parameter Redundancy: Spectral Encoding Helps](https://mlsanity.com/paper/2506.16787/) (2025)
- [Retrieval-Augmented Recommendation Explanation Generation with Hierarchical Aggregation](https://mlsanity.com/paper/2507.09188/) (2025)
- [Learning to Shop Like Humans: A Review-driven Retrieval-Augmented Recommendation Framework with LLMs](https://mlsanity.com/paper/2509.00698/) (2025)
- [Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations](https://mlsanity.com/paper/2510.17256/) (2025)
- [Structure Guided Retrieval-Augmented Generation for Factual Queries](https://mlsanity.com/paper/2604.22843/) (2026)
- [Mixture-of-Experts Knowledge Graph Retrieval-Augmented Generation for Multi-Agent LLM-based Recommendation](https://mlsanity.com/paper/2605.28175/) (2026)
- [Mini-Game Lifetime Value Prediction in WeChat](https://mlsanity.com/paper/2506.11037/) (2025)
- [Grounded by Experience: Generative Healthcare Prediction Augmented with Hierarchical Agentic Retrieval](https://mlsanity.com/paper/2511.13293/) (2025)
- [Generative Archetype-Grounded Item Representations for Sequential Recommendation](https://mlsanity.com/paper/2606.11023/) (2026)
- [RES-MR: Risk-Aware Reasoning for Explainable and Safe Medication Recommendation](https://mlsanity.com/paper/2610.14474/) (2026)
- [Can LLMs Fool Graph Learning? Exploring Universal Adversarial Attacks on Text-Attributed Graphs](https://mlsanity.com/paper/2603.21155/) (2026)
- [MMP-Refer: Multimodal Path Retrieval-augmented LLMs For Explainable Recommendation](https://mlsanity.com/paper/2604.03666/) (2026)
- [DCGL: Dual-Channel Graph Learning with Large Language Models for Knowledge-Aware Recommendation](https://mlsanity.com/paper/2605.07314/) (2026)
- [Pairwise Ranking Outperforms Single-Action RL for Offline Explanation Selection: A Practical Lesson](https://mlsanity.com/paper/2608.18531/) (2026)
- [HGP:An on-device personalized agent memory via hybrid graph storage](https://mlsanity.com/paper/2610.10071/) (2026)

## Abstract (arXiv)

Explainable recommendation has demonstrated significant advantages in informing users about the logic behind recommendations, thereby increasing system transparency, effectiveness, and trustworthiness. To provide personalized and interpretable explanations, existing works often combine the generation capabilities of large language models (LLMs) with collaborative filtering (CF) information. CF information extracted from the user-item interaction graph captures the user behaviors and preferences, which is crucial for providing informative explanations. However, due to the complexity of graph structure, effectively extracting the CF information from graphs still remains a challenge. Moreover, existing methods often struggle with the integration of extracted CF information with LLMs due to its implicit representation and the modality gap between graph structures and natural language explanations. To address these challenges, we propose G-Refer, a framework using graph retrieval-augmented large language models (LLMs) for explainable recommendation. Specifically, we first employ a hybrid graph retrieval mechanism to retrieve explicit CF signals from both structural and semantic perspectives. The retrieved CF information is explicitly formulated as human-understandable text by the proposed graph translation and accounts for the explanations generated by LLMs. To bridge the modality gap, we introduce knowledge pruning and retrieval-augmented fine-tuning to enhance the ability of LLMs to process and utilize the retrieved CF information to generate explanations. Extensive experiments show that G-Refer achieves superior performance compared with existing methods in both explainability and stability. Codes and data are available at https://github.com/Yuhan1i/G-Refer.