mlsanityData to 14 Oct 2026

Towards Efficient Hypergraph and Multi-LLM Agent Recommender Systems

arXiv 2512.06590 ↗First posted 6 Dec 2025cs.IR cs.AI cs.MAcited by 0

On the map: LLM agents and tool use → Agentic recommender systems

Abstract (arXiv)

Recommender Systems (RSs) have become the cornerstone of various applications such as e-commerce and social media platforms. The evolution of RSs is paramount in the digital era, in which personalised user experience is tailored to the user's preferences. Large Language Models (LLMs) have sparked a new paradigm - generative retrieval and recommendation. Despite their potential, generative RS methods face issues such as hallucination, which degrades the recommendation performance, and high computational cost in practical scenarios. To address these issues, we introduce HGLMRec, a novel Multi-LLM agent-based RS that incorporates a hypergraph encoder designed to capture complex, multi-behaviour relationships between users and items. The HGLMRec model retrieves only the relevant tokens during inference, reducing computational overhead while enriching the retrieval context. Experimental results show performance improvement by HGLMRec against state-of-the-art baselines at lower computational cost.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agentic recommender systems, one of 16 directions in LLM agents and tool use. Builds on 25 of its 37 references that are in the map. First posted on arXiv 6 Dec 2025.

Builds on

25 of 37 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations31 Aug

Other references · 24

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2024Qwen2.5 Technical Reportcited by 4,942
  3. 2018How Powerful are Graph Neural Networks?cited by 4,393
  4. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  5. 2018Self-Attentive Sequential Recommendationcited by 1,184
  6. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
  7. 2024Mixture-of-Agents Enhances Large Language Model Capabilitiescited by 438
  8. 2019Hypergraph Convolution and Hypergraph Attentioncited by 228
  9. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  10. 2024Let Your Graph Do the Talking: Encoding Structured Data for LLMscited by 118
  11. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  12. 2024Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoningcited by 88
12 more
  1. 2023Leveraging Large Language Models for Sequential Recommendationcited by 78
  2. 2023Generative Recommendation: Towards Next-generation Recommender Paradigmcited by 70
  3. 2023GenRec: Large Language Model for Generative Recommendationcited by 63
  4. 2024Large Language Models for Next Point-of-Interest Recommendationcited by 40
  5. 2024Harnessing Large Language Models for Text-Rich Sequential Recommendationcited by 38
  6. 2023Large Language Model Augmented Narrative Driven Recommendationscited by 31
  7. 2022Graph Neural Networks with Adaptive Readoutscited by 29
  8. 2021Zero-Shot Recommendation as Language Modelingcited by 28
  9. 2024Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendationcited by 15
  10. 2024DynLLM: When Large Language Models Meet Dynamic Graph Recommendationcited by 12
  11. 2021Session-based Recommendation with Self-Attention Networkscited by 3
  12. 2024HMAR: Hierarchical Masked Attention for Multi-Behaviour Recommendationcited by 2

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  1. 2025EAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration
  2. 2025DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation
  3. 2025A Survey on LLM-powered Agents for Recommender Systems
  4. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval
  5. 2023LLMRec: Large Language Models with Graph Augmentation for Recommendation
  6. 2023On Generative Agents in Recommendation
  7. 2024All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
  8. 2025AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems