ProMap: Datasets for Product Mapping in E-commerce

TL;DR

ProMap introduces two comprehensive datasets for product mapping in e-commerce, including images and descriptions, addressing limitations of existing datasets and enabling more accurate identification of matching and non-matching product pairs.

Contribution

The paper presents two new detailed datasets for product mapping, with carefully selected similar non-matching pairs, enhancing research and model training in e-commerce product matching.

Findings

Datasets contain images and textual descriptions, including specifications.

Non-matching pairs are carefully selected to be very similar, increasing difficulty.

Baseline models demonstrate the datasets' complexity and usefulness.

Abstract

The goal of product mapping is to decide, whether two listings from two different e-shops describe the same products. Existing datasets of matching and non-matching pairs of products, however, often suffer from incomplete product information or contain only very distant non-matching products. Therefore, while predictive models trained on these datasets achieve good results on them, in practice, they are unusable as they cannot distinguish very similar but non-matching pairs of products. This paper introduces two new datasets for product mapping: ProMapCz consisting of 1,495 Czech product pairs and ProMapEn consisting of 1,555 English product pairs of matching and non-matching products manually scraped from two pairs of e-shops. The datasets contain both images and textual descriptions of the products, including their specifications, making them one of the most complete datasets for product mapping. Additionally, the non-matching products were selected in two phases, creating two types of non-matches -- close non-matches and medium non-matches. Even the medium non-matches are pairs of products that are much more similar than non-matches in other datasets -- for example, they still need to have the same brand and similar name and price. After simple data preprocessing, several machine learning algorithms were trained on these and two the other datasets to demonstrate the complexity and completeness of ProMap datasets. ProMap datasets are presented as a golden standard for further research of product mapping filling the gaps in existing ones.