Retailers rely heavily on product matching to better serve their customers, and to improve their modelling and forecasting. Product matching refers to the process of identifying similar or identical products across different data sources. This is a challenging problem as standardized unique identifiers are not used consistently across retailers, and product descriptions and characteristics vary across data collections. In this paper we present and discuss lessons learned from product matching in a real world application. We propose an evaluation framework where we investigate and compare the use of traditional machine learning methods and deep learning methods on public and proprietary datasets for product matching. Our findings show that traditional machine learning methods perform well on this task and a practitioner should investigate these methods first.
Article ID: 2021L25
Publisher: Canadian Artificial Intelligence Association