mlsanityData to 7 Oct 2026

Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generation

arXiv 2409.18003 ↗First posted 26 Sep 2024cs.IRcited by 4

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

Abstract (arXiv)

Tourism Recommender Systems (TRS) have traditionally focused on providing personalized travel suggestions, often prioritizing user preferences without considering broader sustainability goals. Integrating sustainability into TRS has become essential with the increasing need to balance environmental impact, local community interests, and visitor satisfaction. This paper proposes a novel approach to enhancing TRS for sustainable city trips using Large Language Models (LLMs) and a modified Retrieval-Augmented Generation (RAG) pipeline. We enhance the traditional RAG system by incorporating a sustainability metric based on a city's popularity and seasonal demand during the prompt augmentation phase. This modification, called Sustainability Augmented Reranking (SAR), ensures the system's recommendations align with sustainability goals. Evaluations using popular open-source LLMs, such as Llama-3.1-Instruct-8B and Mistral-Instruct-7B, demonstrate that the SAR-enhanced approach consistently matches or outperforms the baseline (without SAR) across most metrics, highlighting the benefits of incorporating sustainability into TRS.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in RAG for recommendation, one of 15 directions in Retrieval-augmented generation. Builds on 33 of its 63 references that are in the map. First posted on arXiv 26 Sep 2024.

Builds on

33 of 63 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation11 Mar

Other references · 32

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2024The Llama 3 Herd of Modelscited by 14,192
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  5. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,175
  6. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  7. 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,414
  8. 2019ZeRO: Memory Optimizations Toward Training Trillion Parameter Modelscited by 1,278
  9. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
  10. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 719
  11. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 695
  12. 2023Is ChatGPT a Good NLG Evaluator? A Preliminary Studycited by 420
20 more
  1. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 408
  2. 2023TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationcited by 336
  3. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 241
  4. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 192
  5. 2023Adapting Large Language Models by Integrating Collaborative Semantics for Recommendationcited by 171
  6. 2023Uncovering ChatGPT's Capabilities in Recommender Systemscited by 160
  7. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 141
  8. 2023Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitationscited by 96
  9. 2023GenRec: Large Language Model for Generative Recommendationcited by 62
  10. 2021RevCore: Review-augmented Conversational Recommendationcited by 47
  11. 2024Financial Report Chunking for Effective Retrieval Augmented Generationcited by 46
  12. 2021TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providerscited by 31
  13. 2024Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMscited by 27
  14. 2021SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarioscited by 23
  15. 2024Synthetic Test Collections for Retrieval Evaluationcited by 18
  16. 2024CTourLLM: Enhancing LLMs with Chinese Tourism Knowledgecited by 7
  17. 2023Lending Interaction Wings to Recommender Systems with Conversational Agentscited by 6
  18. 2023Towards Individual and Multistakeholder Fairness in Tourism Recommender Systemscited by 5
  19. 2024Modeling Sustainable City Trips: Integrating CO2e Emissions, Popularity, and Seasonality into Tourism Recommender Systemscited by 3
  20. 2023Empowering Few-Shot Recommender Systems with Large Language Models -- Enhanced Representationscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planningcited by 12
  2. 2026Autonomous Information Seeking: A Roadmap for Agentic Recommender Systemscited by 3
  3. 2026LLMGreenRec: LLM-Based Multi-Agent Recommender System for Sustainable E-Commercecited by 1
  4. 2025Design and Implementation of a Secure RAG-Enhanced AI Chatbot for Smart Tourism Customer Service: Defending Against Prompt Injection Attacks -- A Case Study of Hsinchu, Taiwancited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026TRACE: A Conversational Framework for Sustainable Tourism Recommendation with Agentic Counterfactual Explanations
  2. 2025Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourism
  3. 2024RAG-Optimized Tibetan Tourism LLMs: Enhancing Accuracy and Personalization
  4. 2024QCG-Rerank: Chunks Graph Rerank with Query Expansion in Retrieval-Augmented LLMs for Tourism Domain
  5. 2025WebRec: Enhancing LLM-based Recommendations with Attention-guided RAG from Web
  6. 2025Quality Assurance for LLM-RAG Systems: Empirical Insights from Tourism Application Testing
  7. 2025Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation
  8. 2025Balancing Fine-tuning and RAG: A Hybrid Strategy for Dynamic LLM Recommendation Updates