Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data
This work addresses the challenge of standardizing product data for e-commerce applications, but it appears incremental as it builds on existing methods and datasets.
The paper tackled the problem of extracting structured product information from unstructured multilingual web data, achieving robust attribute prediction across online shops and languages, and enabling product taxonomy matching between retailers.
Extracting structured information from unstructured data is one of the key challenges in modern information retrieval applications, including e-commerce. Here, we demonstrate how recent advances in machine learning, combined with a recently published multilingual data set with standardized fine-grained product category information, enable robust product attribute extraction in challenging transfer learning settings. Our models can reliably predict product attributes across online shops, languages, or both. Furthermore, we show that our models can be used to match product taxonomies between online retailers.