TRACE: A Conversational Framework for Sustainable Tourism Recommendation with Agentic Counterfactual Explanations
For travel recommendation systems, TRACE addresses the overlooked problem of reinforcing unsustainable tourism by integrating sustainability nudging without sacrificing user relevance.
TRACE introduces a multi-agent LLM framework for sustainable tourism recommendations that uses counterfactual explanations and clarifying questions to nudge users toward greener choices. User studies show it maintains recommendation quality while effectively promoting sustainable decisions.
Traditional conversational travel recommender systems primarily optimize for user relevance and convenience, often reinforcing popular, overcrowded destinations and carbon-intensive travel choices. To address this, we present TRACE (Tourism Recommendation with Agentic Counterfactual Explanations), a multi-agent, LLM-based framework that promotes sustainable tourism through interactive nudging. TRACE uses a modular orchestrator-worker architecture where specialized agents elicit latent sustainability preferences, construct structured user personas, and generate recommendations that balance relevance with environmental impact. A key innovation lies in its use of agentic counterfactual explanations and LLM-driven clarifying questions, which together surface greener alternatives and refine understanding of intent, fostering user reflection without coercion. User studies and semantic alignment analyses demonstrate that TRACE effectively supports sustainable decision-making while preserving recommendation quality and interactive responsiveness. TRACE is implemented on Google's Agent Development Kit, with full code, Docker setup, prompts, and a publicly available demo video to ensure reproducibility. A project summary, including all resources, prompts, and demo access, is available at https://ashmibanerjee.github.io/trace-chatbot.