mlsanityData to 14 Oct 2026

MARC: Multimodal and Multi-Task Agentic Retrieval-Augmented Generation for Cold-Start Recommender System

arXiv 2511.08181 ↗First posted 11 Nov 2025cs.IR cs.AIcited by 1

On the map: LLM reasoning → LLM reasoning for recommendation

also in 2 more
Abstract (arXiv)

Recommender systems (RS) are currently being studied to mitigate limitations during cold-start conditions by leveraging modality information or introducing Agent concepts based on the exceptional reasoning capabilities of Large Language Models (LLMs). Meanwhile, food and beverage recommender systems have traditionally used knowledge graph and ontology concepts due to the domain's unique data attributes and relationship characteristics. On this background, we propose MARC, a multimodal and multi-task cocktail recommender system based on Agentic Retrieval-Augmented Generation (RAG) utilizing graph database under cold-start conditions. The proposed system generates high-quality, contextually appropriate answers through two core processes: a task recognition router and a reflection process. The graph database was constructed by processing cocktail data from Kaggle, and its effectiveness was evaluated using 200 manually crafted questions. The evaluation used both LLM-as-a-judge and human evaluation to demonstrate that answers generated via the graph database outperformed those from a simple vector database in terms of quality. The code is available at https://github.com/diddbwls/cocktail_rec_agentrag

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning. Builds on 27 of its 45 references that are in the map. First posted on arXiv 11 Nov 2025.

Builds on

27 of 45 references.

References · 27

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  4. 2025Qwen2.5-VL Technical Reportcited by 5,587
  5. 2023A Survey of Large Language Modelscited by 2,576
  6. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
  7. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,332
  8. 2023Large Language Modelscited by 780
  9. 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAGcited by 249
  10. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 172
  11. 2023Recommender AI Agent: Integrating Large Language Models for Interactive Recommendationscited by 131
  12. 2023RecMind: Large Language Model Powered Agent For Recommendationcited by 129
15 more
  1. 2023Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisitedcited by 124
  2. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systemscited by 116
  3. 2022A Survey on the Fairness of Recommender Systemscited by 102
  4. 2024GRAG: Graph Retrieval-Augmented Generationcited by 78
  5. 2023LLM-Rec: Personalized Recommendation via Prompting Large Language Modelscited by 77
  6. 2025Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendationcited by 57
  7. 2023Zero-Shot Next-Item Recommendation using Large Pretrained Language Modelscited by 53
  8. 2025A Survey on LLM-powered Agents for Recommender Systemscited by 50
  9. 2025When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generationcited by 48
  10. 2019Bidirectional Attentive Memory Networks for Question Answering over Knowledge Basescited by 30
  11. 2023RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agentscited by 30
  12. 2024GeAR: Graph-enhanced Agent for Retrieval-augmented Generationcited by 17
  13. 2024Knowledge Graph Enhanced Language Agents for Recommendationcited by 11
  14. 2025The Term 'Agent' Has Been Diluted Beyond Utility and Requires Redefinitioncited by 6
  15. 2025Query-Aware Graph Neural Networks for Enhanced Retrieval-Augmented Generationcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Autonomous Information Seeking: A Roadmap for Agentic Recommender Systemscited by 4

Similar, not cited

Close in wording, no citation either way

  1. 2025Adaptive Candidate Retrieval with Dynamic Knowledge Graph Construction for Cold-Start Recommendation
  2. 2025AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems
  3. 2026Mixture-of-Experts Knowledge Graph Retrieval-Augmented Generation for Multi-Agent LLM-based Recommendation
  4. 2025Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models
  5. 2025A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
  6. 2025ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
  7. 2024RAGSys: Item-Cold-Start Recommender as RAG System
  8. 2025The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems