TRACE is a multi-agent LLM-based conversational framework that generates sustainable tourism recommendations via counterfactual explanations and clarifying questions to balance user relevance with environmental impact.
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Optimal preference elicitation in conversational recommenders is stage-dependent (attributes early, items later), and a MoE model trained on a new annotated dataset improves offline recommendation and response quality.
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TRACE: A Conversational Framework for Sustainable Tourism Recommendation with Agentic Counterfactual Explanations
TRACE is a multi-agent LLM-based conversational framework that generates sustainable tourism recommendations via counterfactual explanations and clarifying questions to balance user relevance with environmental impact.
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When and How to Ask: Dynamic Preference Elicitation Strategies for Conversational Recommendation
Optimal preference elicitation in conversational recommenders is stage-dependent (attributes early, items later), and a MoE model trained on a new annotated dataset improves offline recommendation and response quality.