MAS4POI: a Multi-Agents Collaboration System for Next POI Recommendation
LLM-based Multi-Agent Systems have potential benefits of complex decision-making tasks management across various domains but their applications in the next Point-of-Interest (POI) recommendation remain underexplored. This paper proposes a novel MAS4POI system designed to enhance next POI recommendations through multi-agent interactions. MAS4POI supports Large Language Models (LLMs) specializing in distinct agents such as DataAgent, Manager, Analyst, and Navigator with each contributes to a collaborative process of generating the next POI recommendations.The system is examined by integrating six distinct LLMs and evaluated by two real-world datasets for recommendation accuracy improvement in real-world scenarios. Our code is available at https://github.com/yuqian2003/MAS4POI.
Code (1)
Tasks
Decision MakingManagementSimilar Papers 제목 키워드 기반
MACRec: a Multi-Agent Collaboration Framework for Recommendation
LLM-based agents have gained considerable attention for their decision-making skills and ability to handle complex tasks. Recognizing the current gap in leveraging agent capabilities for multi-agent collaboration in reco…
Conversational RecommendationDecision MakingExplanation GenerationRecommendation Systems+1MATCHA: Can Multi-Agent Collaboration Build a Trustworthy Conversational Recommender?
In this paper, we propose a multi-agent collaboration framework called MATCHA for conversational recommendation system, leveraging large language models (LLMs) to enhance personalization and user engagement. Users can re…
Conversational RecommendationDiversityRecommendation SystemsRe-RankingMulti-Agent Collaborative Filtering: Orchestrating Users and Items for Agentic Recommendations
Agentic recommendations cast recommenders as large language model (LLM) agents that can plan, reason, use tools, and interact with users of varying preferences in web applications. However, most existing agentic recommen…
Collaborative FilteringAgentRec: Next-Generation LLM-Powered Multi-Agent Collaborative Recommendation with Adaptive Intelligence
Interactive conversational recommender systems have gained significant attention for their ability to capture user preferences through natural language interactions. However, existing approaches face substantial challeng…
Robust Multi-Agent Multi-Armed Bandits
Recent works have shown that agents facing independent instances of a stochastic $K$-armed bandit can collaborate to decrease regret. However, these works assume that each agent always recommends their individual best-ar…
Distributed ComputingMulti-Armed BanditsRecommendation Systems