Product Attribute
Product attributes, crucial for e-commerce and various other applications, are the focus of intense research aimed at efficiently extracting, analyzing, and utilizing them from diverse data sources like product descriptions, reviews, and images. Current research emphasizes leveraging large language models (LLMs) and advanced techniques like product quantization and graph neural networks to improve the accuracy and efficiency of attribute extraction and related tasks such as question answering and recommendation systems. This work is significant because accurate and comprehensive product attribute information is essential for enhancing search, personalization, and overall user experience in online marketplaces and improving product design based on customer feedback.
Papers
KaPQA: Knowledge-Augmented Product Question-Answering
Swetha Eppalapally, Daksh Dangi, Chaithra Bhat, Ankita Gupta, Ruiyi Zhang, Shubham Agarwal, Karishma Bagga, Seunghyun Yoon, Nedim Lipka, Ryan A. Rossi, Franck Dernoncourt
Prompting for products: Investigating design space exploration strategies for text-to-image generative models
Leah Chong, I-Ping Lo, Jude Rayan, Steven Dow, Faez Ahmed, Ioanna Lykourentzou
PQCache: Product Quantization-based KVCache for Long Context LLM Inference
Hailin Zhang, Xiaodong Ji, Yilin Chen, Fangcheng Fu, Xupeng Miao, Xiaonan Nie, Weipeng Chen, Bin Cui
An Empirical Comparison of Generative Approaches for Product Attribute-Value Identification
Kassem Sabeh, Robert Litschko, Mouna Kacimi, Barbara Plank, Johann Gamper