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    大语言模型驱动的选址推荐系统

    Large Language Model Powered Site Selection Recommender System

    • 摘要: 选址作为商业决策和城市基础设施规划的核心环节,对实体店铺、城市基础设施能否发挥预期效用具有重要作用. 现有的选址推荐系统数据服务编排较为固定,无法对不同用户需求系统做出及时调整,应用场景受限,人机交互的系统灵活性和可扩展性差. 最近,以GPT-4为代表的大语言模型(large language model,LLM)展现出了强大的意图理解、任务编排、代码生成和工具使用能力,能够完成传统推荐模型难以兼顾的任务,为重塑推荐流程、实现一体化的推荐服务提供了新的机遇. 然而,一方面选址推荐兼具传统推荐共有的挑战;另一方面,由于其基于空间数据,具有独特的挑战. 在这一背景下,提出了大语言模型驱动的选址推荐系统. 首先,拓展了选址推荐的场景,提出了根据位置寻找合适店铺类型的场景推荐任务,结合了协同过滤算法和空间预训练模型. 其次,构建了由大语言模型驱动的选址决策引擎. 语言模型本身在处理空间相关的任务上存在诸多缺陷,例如缺少空间感知能力、无法理解具体位置、会虚构地名地址等. 提出了一种在语言模型框架处理空间任务的机制,通过地理编码、逆编码、地名地址解析等工具提升模型的空间感知能力并避免地址虚构问题,结合选址推荐模型、场景推荐模型、外部知识库、地图可视化完成选址推荐中的多样化任务. 实现选址任务的智能规划、执行与归因,提升了空间服务系统的交互体验,为未来人工智能驱动的选址推荐系统提供新的设计和实现思路.

       

      Abstract: 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 LLM-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. We achieve intelligent planning, execution, and attribution for site selection tasks, enhance the interaction experience of spatial service systems, and provide new design and implementation ideas for future AI-driven site selection recommender systems.

       

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