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无人零售店的智能客户服务通过大型语言模型来增强

Wang Wang¹, Ping Zhang², Changxia Sun³

¹College of Information and Digital Engineering, Luoyang Vocational College of Science and Technology, Luoyang, 471023, Henan, China.

Scientific reports

|August 27, 2024

概括

本研究介绍了一个AI框架,用于在无人零售商店的智能客户服务. 它使用视觉识别和微调的大型语言模型 (LLM) 来了解客户需求并有效地推产品.

科学领域:

计算机科学计算机科学
人工智能的人工智能
零售技术零售技术零售技术

背景情况:

无人零售商店需要先进的客户服务解决方案,以了解购物者的需求和指导产品选择.
当前的系统往往缺乏个性化交互和有效提供实时建议的复杂性.

研究的目的:

提出一个端到端 (客户到货架) 软件服务框架,用于无人零售的智能客户服务.
整合视觉识别和大型语言模型 (LLM) 以提高客户理解和产品推.

主要方法:

利用基于深度神经网络的图像识别来进行库存单位 (SKU) 的对象检测.
开发一种新的方法来微调LLM,使用元启发式优化来利用有限的数据进行高效的参数更新.
实施一个智能推系统,由微调的LLM为个性化客户偏好分析提供动力.

主要成果:

拟议的LLM微调方法在有限的下游数据集上显示出与最先进的技术相比更高的效率.
综合框架能够成功地了解个性化的客户需求,并提供量身定制的购物建议.
实验结果验证了框架能够创造无购物体验的能力.

结论:

开发的客户到货架框架为无人零售环境中的智能客户服务提供了有效的解决方案.
微调LLM与元启发式优化是数据稀缺领域的可行策略.
该系统通过个性化交互和高效的产品指导来提高客户满意度.

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