TRACE: A Conversational Framework for Sustainable Tourism Recommendation with Agentic Counterfactual Explanations
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 sustainabi
Record details
Published: 14 April 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy · Environment
Retrieved: 14 July 2026
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ethics.ai (14 April 2026), “TRACE: A Conversational Framework for Sustainable Tourism Recommendation with Agentic Counterfactual Explanations,” evidence record 5869, https://ethics.ai/record/5869 (originally published by arXiv).
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