MAP: Multi-user Personalization with Collaborative LLM-powered Agents

• URL: http://arxiv.org/abs/2503.12757v2
• Date: Tue, 18 Mar 2025 19:15:44 GMT
• Title: MAP: Multi-user Personalization with Collaborative LLM-powered Agents
• Abstract summary: We introduce a user-centered workflow for multi-user personalization comprising three stages: Reflection, Analysis, and Feedback.<n> MAP -- a textbfMulti-textbfAgent system for multi-user textbfPersonalization -- to operationalize this workflow.<n>Our user study findings highlight MAP's effectiveness and usability for conflict resolution while emphasizing the importance of user involvement in resolution verification and failure management.
• Score: 11.027068698393027
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The widespread adoption of Large Language Models (LLMs) and LLM-powered agents in multi-user settings underscores the need for reliable, usable methods to accommodate diverse preferences and resolve conflicting directives. Drawing on conflict resolution theory, we introduce a user-centered workflow for multi-user personalization comprising three stages: Reflection, Analysis, and Feedback. We then present MAP -- a \textbf{M}ulti-\textbf{A}gent system for multi-user \textbf{P}ersonalization -- to operationalize this workflow. By delegating subtasks to specialized agents, MAP (1) retrieves and reflects on relevant user information, while enhancing reliability through agent-to-agent interactions, (2) provides detailed analysis for improved transparency and usability, and (3) integrates user feedback to iteratively refine results. Our user study findings (n=12) highlight MAP's effectiveness and usability for conflict resolution while emphasizing the importance of user involvement in resolution verification and failure management. This work highlights the potential of multi-agent systems to implement user-centered, multi-user personalization workflows and concludes by offering insights for personalization in multi-user contexts.

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