  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.
