Large Language Models Powered Site Selection Recommender System

Site selection, as a core link in business decisions and urban infrastructure planning, plays a significant role in whether physical stores and urban infrastructure can perform as expected. The existing site selection recommender system's data service orchestration is rather fixed and unable to make timely adjustments to different user needs. Its application scenarios are limited, and the system's flexibility and scalability in human-computer interaction are poor. Recently, Large Language Models (LLMs) such as GPT-4 have demonstrated powerful capabilities in intent understanding, task orchestration, code generation, and tool usage. They can accomplish tasks that traditional recommendation models struggle to balance, providing new opportunities for reshaping the recommendation process and implementing integrated recommendation services. However, site selection recommendation not only shares the common challenges of traditional recommendations but also presents unique challenges due to its reliance on spatial data. Against this backdrop, we propose a LLMs-powered site selection recommendation system. Firstly, we expand the scenarios of site selection recommendation and propose a scene recommendation task of finding suitable store types based on location, combining collaborative filtering algorithms and spatial pre-training models. Secondly, we construct a site selection decision engine driven by a large language model. The language model itself has many defects in dealing with space-related tasks, such as the lack of spatial awareness, inability to understand specific locations, and the tendency to fabricate place names and addresses. In this paper, we propose a mechanism for handling spatial tasks within the language model framework. By utilizing geocoding, reverse geocoding, and address resolution tools, we enhance the model's spatial awareness and prevent address fabrication issues. In combination with site selection recommendation models, scenario recommendation models, external knowledge bases, and map visualization, we accomplish diverse tasks in site selection recommendations. This paper achieves intelligent planning, execution, and attribution for site selection tasks, enhances the interaction experience of spatial service systems, and provides new design and implementation ideas for future AI-driven site selection recommender systems.