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

TL;DR

This paper introduces a novel method for improving tourism recommender systems by integrating sustainability metrics into Large Language Model-based suggestions, promoting environmentally and socially responsible city travel recommendations.

Contribution

It proposes a Sustainability Augmented Reranking (SAR) method that incorporates sustainability considerations into retrieval-augmented generation for tourism recommendations.

Findings

SAR improves recommendation relevance and sustainability alignment.

SAR outperforms baseline models on key metrics.

Incorporating sustainability metrics enhances TRS effectiveness.

Abstract

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.