mlsanityData to 7 Oct 2026

LLMGreenRec: LLM-Based Multi-Agent Recommender System for Sustainable E-Commerce

arXiv 2603.11025 ↗First posted 11 Mar 2026cs.MA cs.IRcited by 1

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

Abstract (arXiv)

Rising environmental awareness in e-commerce necessitates recommender systems that not only guide users to sustainable products but also minimize their own digital carbon footprints. Traditional session-based systems, optimized for short-term conversions, often fail to capture nuanced user intents for eco-friendly choices, perpetuating a gap between green intentions and actions. To tackle this, we introduce LLMGreenRec, a novel multi-agent framework that leverages Large Language Models (LLMs) to promote sustainable consumption. Through collaborative analysis of user interactions and iterative prompt refinement, LLMGreenRec's specialized agents deduce green-oriented user intents and prioritize eco-friendly product recommendations. Notably, this intent-driven approach also reduces unnecessary interactions and energy consumption. Extensive experiments on benchmark datasets validate LLMGreenRec's effectiveness in recommending sustainable products, demonstrating a robust solution that fosters a responsible digital economy.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Agentic recommender systems, one of 16 directions in LLM agents and tool use. Builds on 23 of its 46 references that are in the map. First posted on arXiv 11 Mar 2026.

Builds on

23 of 46 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024MACRec: a Multi-Agent Collaboration Framework for Recommendation23 Feb

Other references · 22

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2015Session-based Recommendations with Recurrent Neural Networkscited by 685
  4. 2023Automatic Prompt Optimization with "Gradient Descent" and Beam Searchcited by 628
  5. 2018Session-based Recommendation with Graph Neural Networkscited by 312
  6. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 307
  7. 2017Neural Attentive Session-based Recommendationcited by 237
  8. 2023GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretationcited by 101
  9. 2016Improved Recurrent Neural Networks for Session-based Recommendationscited by 86
  10. 2021Global Context Enhanced Graph Neural Networks for Session-based Recommendationcited by 81
  11. 2023A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systemscited by 73
  12. 2023Zero-Shot Next-Item Recommendation using Large Pretrained Language Modelscited by 53
10 more
  1. 2022Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Networkcited by 38
  2. 2023Large Language Models for Intent-Driven Session Recommendationscited by 30
  3. 2023Large Language Model Can Interpret Latent Space of Sequential Recommendercited by 19
  4. 2020A Re-visit of the Popularity Baseline in Recommender Systemscited by 18
  5. 2023Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendationcited by 14
  6. 2019Improving End-to-End Sequential Recommendations with Intent-aware Diversificationcited by 12
  7. 2021Learning Multi-granularity User Intent Unit for Session-based Recommendationcited by 11
  8. 2024Advancing Sustainability via Recommender Systems: A Surveycited by 6
  9. 2024Recommender Systems for Sustainability: Overview and Research Issuescited by 5
  10. 2024Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generationcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Continual Graph Memory for Adaptive Recommendation under Intent Driftcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Trading Engagement for Sustainability: Carbon-Aware Re-ranking for E-commerce Recommendations
  2. 2026TRACE: A Conversational Framework for Sustainable Tourism Recommendation with Agentic Counterfactual Explanations
  3. 2026MACS: A Hybrid Multi-Agent Framework for Reliable Conversational E-Commerce Recommendation
  4. 2025A Survey on LLM-powered Agents for Recommender Systems
  5. 2025AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems
  6. 2025Multi-Agent Collaborative Filtering: Orchestrating Users and Items for Agentic Recommendations
  7. 2026RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents
  8. 2025Promoting Sustainable Web Agents: Benchmarking and Estimating Energy Consumption through Empirical and Theoretical Analysis