Data mining for managing stock keeping units

Stock keeping units (SKUs) are compact identifiers representing billable products in the inventory for sale. Merchants often assign SKUs by transforming the text descriptions of the products following various implicit SKU encoding schemes. In the transformation process, the text description of a product is divided into character blocks, some blocks are skipped, and the remaining are abbreviated and aligned into the SKU in a new order. In this paper, we describe an instance-based data mining approach for automatically (i) extracting likely underlying SKU encoding schemes as explicit formal encoding and alignment patterns, (ii) inferring a list of likely SKUs given the text description of a new product, and (iii) inferring a list of likely text descriptions given the SKU of a product with missing text description. We have built a prototype system for testing on real-world datasets, and the empirical results confirm the effectiveness of the approach.