Large-scale product classification for efficient matching in procurement systems

Abstract

We consider the problem of recommending relevant suppliers given detailed request context in a procurement setting. The fundamental recommendation in procurement systems is that a single query has potentially hundreds of relevant suppliers associated. A complicating factor is that, for most suppliers, we do not have a complete listing of product and service offerings, in contrast with most literature in the space of product search. An additional difficulty is introduced by the fact that queries are generated by users operating within large procurement organizations, each building queries in idiosyncratic but internally consistent ways, and each organizing activities according to a unique internal product taxonomy. The central research question that we aim to address is: can we utilize this vast but inconsistently structured set of product data that allows us to derive semantic meaning across users and contexts? We propose several fully and semi-supervised approaches and benchmark them using a proprietary dataset that includes large-scale procurement data as well as supplier-provided catalogs. Finally, and uniquely, we experimentally validate the performance of our preferred model in a live production setting.