基于机器学习的零售创新:虚拟试装、扫描和推荐

Malhar Bangdiwala, Sakshi Mahadik, Yashvi Mehta, A. Salunke
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引用次数: 0

摘要

本文讨论了在零售业中越来越多地使用机器学习(ML)模型来改善客户的购物体验。重点是虚拟试验室、自助结账和个性化推荐。虚拟试衣间可以让顾客虚拟地试穿衣服,而自助结账则提供了更快、更方便的结账过程。基于顾客购买历史和偏好的个性化推荐也可以改善整体购物体验。这篇论文回顾了关于机器学习模型使用的文献,并提到了一些先进的模型,这些模型可以将衣服正确地映射到顾客的照片上,并在条形码扫描仪中使用地理位置来避免长时间的排队。
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ML-Based Retail Innovations: Virtual Fitting, Scanning and Recommendations
This paper discusses the increasing use of machine learning (ML) models in the retail industry to improve the shopping experience of customers. The focus is on virtual trial rooms, self-checkout, and personalized recommendations. Virtual trial rooms allow customers to try on clothes virtually, while self-checkout provides a faster and more convenient checkout process. Personalized recommendations based on customers' purchase history and preferences can also improve the overall shopping experience. The paper reviews literature on the use of ML models and mentions advanced models that map clothes correctly to customers' pictures and use geolocation in barcode scanners to avoid long waiting queues.
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