PAE：基于LLM的电子商务时尚趋势产品属性提取
PAE: LLM-based Product Attribute Extraction for E-Commerce Fashion Trends
AI总结：
针对电子商务时尚趋势报告中的产品属性提取问题，提出PAE框架，利用多模态信息从PDF中提取属性，并通过BERT匹配目录，平均F1达92.5%。
摘要：
产品属性提取是电子商务中一个不断发展的领域，其应用包括产品排名、产品推荐、未来品类规划以及改善在线购物客户体验。理解客户需求是在线业务的关键部分，尤其是时尚产品。零售商利用品类规划来确定每家门店和渠道中提供的产品组合，保持对市场动态的响应，并管理库存和目录。其目标是通过正确的渠道，以正确的尺码和颜色提供正确的款式。当购物者找到满足其需求和期望的产品时，他们更有可能再次购买，从而培养客户忠诚度。产品属性是品类规划中的关键因素。在本文中，我们提出了PAE，一种用于从包含文本和图像的PDF格式的未来趋势报告中提取产品属性的算法。大多数现有方法侧重于从标题或产品描述中提取属性，或利用现有产品图像的视觉信息。与先前的工作相比，我们的工作侧重于从PDF文件中提取属性，其中解释了即将到来的时尚趋势。这项工作提出了一个更全面的框架，充分利用不同模态进行属性提取，并帮助零售商提前规划品类。我们的贡献有三个方面：（a）我们开发了PAE，一个从非结构化数据（文本和图像）中提取属性的高效框架；（b）我们提供了一种基于BERT表示的目录匹配方法，利用即将到来的属性值发现现有属性；（c）我们进行了多项基线的广泛实验，并表明PAE是一个有效、灵活且与现有最先进方法相当或更优（平均92.5% F1分数）的属性值提取任务框架。
英文摘要：
Product attribute extraction is an growing field in e-commerce business, with several applications including product ranking, product recommendation, future assortment planning and improving online shopping customer experiences. Understanding the customer needs is critical part of online business, specifically fashion products. Retailers uses assortment planning to determine the mix of products to offer in each store and channel, stay responsive to market dynamics and to manage inventory and catalogs. The goal is to offer the right styles, in the right sizes and colors, through the right channels. When shoppers find products that meet their needs and desires, they are more likely to return for future purchases, fostering customer loyalty. Product attributes are a key factor in assortment planning. In this paper we present PAE, a product attribute extraction algorithm for future trend reports consisting text and images in PDF format. Most existing methods focus on attribute extraction from titles or product descriptions or utilize visual information from existing product images. Compared to the prior works, our work focuses on attribute extraction from PDF files where upcoming fashion trends are explained. This work proposes a more comprehensive framework that fully utilizes the different modalities for attribute extraction and help retailers to plan the assortment in advance. Our contributions are three-fold: (a) We develop PAE, an efficient framework to extract attributes from unstructured data (text and images); (b) We provide catalog matching methodology based on BERT representations to discover the existing attributes using upcoming attribute values; (c) We conduct extensive experiments with several baselines and show that PAE is an effective, flexible and on par or superior (avg 92.5% F1-Score) framework to existing state-of-the-art for attribute value extraction task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
