An adaptive conversational agent for decision support yields more personalized reflective trajectories and integrative language than a cognitive-only baseline in a between-subjects user study.
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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 qualitative study of a provotype shows that adding transparency and control features to AI recommender interfaces helps users understand personalization, address filter bubble concerns, and build trust.
citing papers explorer
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Reflecti-Mate: A Conversational Agent for Adaptive Decision-Making Support Through System 1 and System 2 Thinking
An adaptive conversational agent for decision support yields more personalized reflective trajectories and integrative language than a cognitive-only baseline in a between-subjects user study.
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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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Rethinking User Empowerment in AI Recommender System: Innovating Transparent and Controllable Interfaces
A qualitative study of a provotype shows that adding transparency and control features to AI recommender interfaces helps users understand personalization, address filter bubble concerns, and build trust.