Integration of K-Means Clustering and GPT-4.1 for an AI-Based Strategic Hotel Decision Support System

Abstract

This research develops an artificial intelligence-based strategic decision support system for optimizing hotel revenue management, integrating K-Means Clustering and GPT-4.1. The novelty of this study lies in the combination of daily performance analysis using K-Means for segmentation and narrative-based recommendations generated by GPT-4.1, a novel approach not widely applied in hospitality contexts. This approach not only utilizes data segmentation techniques but also transforms the segmentation results into strategic recommendations that are practical and easily understood by hotel management. The theoretical contribution of this research is the development of an integration method between clustering algorithms and large language models, while its practical contribution is the improvement of operational decision-making efficiency based on data, which can enhance hotel performance and daily revenue. Thus, this research contributes to the development of AI-based systems that can be adapted to other service sectors with similar operational patterns.