A Retail Product Categorisation Dataset
This provides a resource for researchers and practitioners in eCommerce to benchmark methods for product categorization, but it is incremental as it focuses on dataset creation rather than novel algorithms.
The authors tackled the problem of identifying similar products in eCommerce by creating a dataset for predicting product categories from images and descriptions, aiming to improve evaluation of machine learning methods in this domain.
Most eCommerce applications, like web-shops have millions of products. In this context, the identification of similar products is a common sub-task, which can be utilized in the implementation of recommendation systems, product search engines and internal supply logistics. Providing this data set, our goal is to boost the evaluation of machine learning methods for the prediction of the category of the retail products from tuples of images and descriptions.