mlsanityData to 7 Oct 2026

MMP-Refer: Multimodal Path Retrieval-augmented LLMs For Explainable Recommendation

arXiv 2604.03666 ↗First posted 4 Apr 2026cs.IRcited by 0

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

Abstract (arXiv)

Explainable recommendations help improve the transparency and credibility of recommendation systems, and play an important role in personalized recommendation scenarios. At present, methods for explainable recommendation based on large language models(LLMs) often consider introducing collaborative information to enhance the personalization and accuracy of the model, but ignore the multimodal information in the recommendation dataset; In addition, collaborative information needs to be aligned with the semantic space of LLM. Introducing collaborative signals through retrieval paths is a good choice, but most of the existing retrieval path collection schemes use the existing Explainable GNN algorithms. Although these methods are effective, they are relatively unexplainable and not be suitable for the recommendation field. To address the above challenges, we propose MMP-Refer, a framework using MultiModal Retrieval Paths with Retrieval-augmented LLM For Explainable Recommendation. We use a sequential recommendation model based on joint residual coding to obtain multimodal embeddings, and design a heuristic search algorithm to obtain retrieval paths by multimodal embeddings; In the generation phase, we integrated a trainable lightweight collaborative adapter to map the graph encoding of interaction subgraphs to the semantic space of the LLM, as soft prompts to enhance the understanding of interaction information by the LLM. Extensive experiments have demonstrated the effectiveness of our approach. Codes and data are available at https://github.com/pxcstart/MMP-Refer.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG for recommendation, one of 15 directions in Retrieval-augmented generation. Builds on 24 of its 42 references that are in the map. First posted on arXiv 4 Apr 2026.

Builds on

24 of 42 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation18 Feb

Other references · 23

  1. 2017Graph Attention Networkscited by 8,854
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  3. 2018How Powerful are Graph Neural Networks?cited by 4,355
  4. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,196
  5. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  6. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 719
  7. 2020Parameterized Explainer for Graph Neural Networkcited by 423
  8. 2023Is ChatGPT a Good NLG Evaluator? A Preliminary Studycited by 420
  9. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 280
  10. 2018Explainable Recommendation: A Survey and New Perspectivescited by 210
  11. 2020PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networkscited by 210
  12. 2015VBPR: Visual Bayesian Personalized Ranking from Implicit Feedbackcited by 151
11 more
  1. 2021Generative Causal Explanations for Graph Neural Networkscited by 124
  2. 2022Personalized Prompt Learning for Explainable Recommendationcited by 79
  3. 2022GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Gamescited by 42
  4. 2020Neural Collaborative Reasoningcited by 37
  5. 2024XRec: Large Language Models for Explainable Recommendationcited by 34
  6. 2023PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Predictioncited by 25
  7. 2022Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptationcited by 24
  8. 2023PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Trainingcited by 12
  9. 2022HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendationscited by 7
  10. 2022Towards Communication Efficient and Fair Federated Personalized Sequential Recommendationcited by 7
  11. 2023The Problem of Coherence in Natural Language Explanations of Recommendationscited by 4

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