用大模型+可持续性增强推荐，让旅游规划更环保。
Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generation
- 在提示生成阶段引入城市热度与季节需求的可持续性指标
- 相比基线，多指标表现持平或更好，尤其提升可持续性匹配度
- 适合关注绿色旅游、智能推荐的开发者与城市规划者
旅游推荐系统（TRS）传统上聚焦个性化建议，常忽略可持续发展目标。随着环境影响、社区利益与游客满意度之间的平衡日益重要，将可持续性融入TRS成为关键。本文提出一种新方法，利用大语言模型（LLM）和改进的检索增强生成（RAG）流程，增强面向可持续城市旅行的推荐系统。通过在提示增强阶段引入基于城市热度与季节性需求的可持续性指标，我们构建了可持续性增强重排序（SAR）机制，确保推荐结果与可持续目标对齐。在Llama-3.1-Instruct-8B与Mistral-Instruct-7B等开源大模型上的评估表明，SAR增强方法在多数指标上表现与基线持平或更优，验证了可持续性集成的有效性。
原文摘要 · Abstract (English)
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.
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