GoldenBullet: Automated Classification of Product Data in E-commerce

Internet and Web technology starts to penetrate many aspects of our daily life. Its importance as a medium for business transactions will grow exponentially during the next years. In terms of the involved market volume the B2B area will hereby be the most interesting area. Also it will be the place, where the new technology will lead to drastic changes in established customer relationships and business models. B2B market places provide new kinds of services to their clients. Simple 1-1 connections are getting replaced by n-m relationships between customers and vendors. However, this new flexibility in electronic trading also generates serious challenges for the parties that want to realize it. The main problem here is caused by the heterogeneity of information descriptions used by vendors and customers. Intelligent solutions that help to mechanize the process of structuring, classifying, aligning, and personalizing are a key requisite for successfully overcoming the current bottlenecks of B2B electronic commerce. In this paper, we describe a system called GoldenBullet that applies techniques from information retrieval and machine learning to the problem of product data classification. The system helps to mechanize an important and labor-intensive task of content management for B2B Ecommerce.