MAS4POI: a Multi-Agents Collaboration System for Next POI Recommendation
About
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.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| POI Recommendation | Foursquare-NYC Standard Evaluation (test) | Recall@518.3 | 10 | |
| POI Recommendation | Foursquare-TKY Standard Evaluation (test) | Recall@516.7 | 10 | |
| POI Recommendation | Yelp-Open Standard Evaluation (test) | R@510.6 | 10 |