PAE: LLM-based Product Attribute Extraction for E-Commerce Fashion Trends

TL;DR

This paper introduces PAE, a novel multi-modal framework leveraging text and images from PDFs to extract fashion product attributes, aiding retailers in trend forecasting and assortment planning with high accuracy.

Contribution

The paper presents PAE, a comprehensive multi-modal attribute extraction framework from PDFs, and a catalog matching method using BERT, outperforming existing methods with 92.5% F1-score.

Findings

PAE achieves an average 92.5% F1-score in attribute extraction.

The framework effectively utilizes both text and images from PDFs.

Experimental results show PAE outperforms state-of-the-art baselines.

Abstract

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.