Summary of Pae: Llm-based Product Attribute Extraction For E-commerce Fashion Trends, by Apurva Sinha and Ekta Gujral
PAE: LLM-based Product Attribute Extraction for E-Commerce Fashion Trends
by Apurva Sinha, Ekta Gujral
First submitted to arxiv on: 27 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel product attribute extraction algorithm, dubbed PAE, designed to extract information from PDF files containing text and images that describe upcoming fashion trends. This work addresses the need for retailers to plan their assortment in advance by utilizing different modalities such as text, images, and future trend reports. The proposed framework, PAE, is a comprehensive solution that leverages BERT representations to discover existing attributes using upcoming attribute values. The authors evaluate PAE against several state-of-the-art baselines, achieving an average F1-Score of 92.5%, demonstrating its effectiveness in extracting product attributes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how to help online fashion stores choose the right products to sell by using a special algorithm called PAE. This algorithm takes information from PDF files that describe future fashion trends and uses it to figure out what characteristics these products will have, like colors or sizes. The goal is to make sure stores have the right mix of products to attract customers and keep them coming back. |
Keywords
» Artificial intelligence » Bert » F1 score
