TRACE is a new benchmark dataset and evaluation suite for conversational tourism recommenders that requires systems to suggest POIs, cite verifiable review spans, and recover from rejections, revealing a Three-Competency Gap across baselines.
Collab- rec: An llm-based agentic framework for balancing recommendations in tourism.arXiv preprint arXiv:2508.15030, 2025a
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.IR 3years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
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.
The authors propose a conceptual framework integrating stakeholder-LLM alignment methods, social choice-based aggregation for collective decisions, and stakeholder-centric evaluations to achieve fair multi-agent personalization.
citing papers explorer
-
TRACE: Tourism Recommendation with Accountable Citation Evidence
TRACE is a new benchmark dataset and evaluation suite for conversational tourism recommenders that requires systems to suggest POIs, cite verifiable review spans, and recover from rejections, revealing a Three-Competency Gap across baselines.
-
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.
-
Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systems
The authors propose a conceptual framework integrating stakeholder-LLM alignment methods, social choice-based aggregation for collective decisions, and stakeholder-centric evaluations to achieve fair multi-agent personalization.