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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PDQUBO is a new performance-driven QUBO method for feature selection in recommender systems that incorporates counterfactual performance impacts of features and pairs, is model-agnostic, and outperforms prior quantum and some classical baselines on CTR tasks.
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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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Performance-Driven QUBO for Recommender Systems on Quantum Annealers
PDQUBO is a new performance-driven QUBO method for feature selection in recommender systems that incorporates counterfactual performance impacts of features and pairs, is model-agnostic, and outperforms prior quantum and some classical baselines on CTR tasks.