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.
A Multi-Agent Conversational Recommender System.arXiv preprint arXiv:2402.01135, 2024
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PaperFlow proposes a Profiling-Recommending-Adapting framework for longitudinal scientific paper recommendation and evaluates it on a new user-day benchmark with 24 simulated users, outperforming five baselines in ranking, behavioral alignment, and blind human evaluation.
Presents a memory-augmented turn-level satisfaction evaluator and PersTurnBench benchmark that improve agreement with human judgments over generic LLM judges and enable controlled model comparisons via replay.
AdaptSim is an adaptive user simulator for CRS evaluation that combines automatic prompt generation, open actions, controlled text generation, and BFS-based pairwise comparison to produce realistic dialogues and assess system robustness across domains.
A multi-agent LLM recommender boosts perceived novelty and diversity in movie suggestions, with effects shaped by user conscientiousness, extraversion, GenAI experience, and skepticism.
A survey categorizing scaling in LLM reasoning across input size, steps, rounds, training, and future directions, noting that scaling can negatively affect performance.
citing papers explorer
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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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PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams
PaperFlow proposes a Profiling-Recommending-Adapting framework for longitudinal scientific paper recommendation and evaluates it on a new user-day benchmark with 24 simulated users, outperforming five baselines in ranking, behavioral alignment, and blind human evaluation.
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Personalized Turn-Level User Conversation Satisfaction Benchmark
Presents a memory-augmented turn-level satisfaction evaluator and PersTurnBench benchmark that improve agreement with human judgments over generic LLM judges and enable controlled model comparisons via replay.
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Towards Fast Domain Adaptation and Fine-Grained User Simulation for Evaluating Conversational Recommender Systems
AdaptSim is an adaptive user simulator for CRS evaluation that combines automatic prompt generation, open actions, controlled text generation, and BFS-based pairwise comparison to produce realistic dialogues and assess system robustness across domains.
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How Personal Characteristics Shape User Exploration of Diverse Movie Recommendations with a LLM-Based Multi-Agent System
A multi-agent LLM recommender boosts perceived novelty and diversity in movie suggestions, with effects shaped by user conscientiousness, extraversion, GenAI experience, and skepticism.
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A Survey of Scaling in Large Language Model Reasoning
A survey categorizing scaling in LLM reasoning across input size, steps, rounds, training, and future directions, noting that scaling can negatively affect performance.