A Retail Product Categorisation Dataset

TL;DR

This paper introduces a retail product categorisation dataset aimed at improving machine learning models for classifying products based on images and descriptions, facilitating better recommendations and search functionalities.

Contribution

It provides a new dataset specifically designed for evaluating machine learning methods in retail product categorization tasks.

Findings

Dataset enables benchmarking of categorization models

Improves accuracy of product classification systems

Supports development of better recommendation algorithms

Abstract

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.