  Recent years brought an increasing interest in the application of machine
learning algorithms in e-commerce, omnichannel marketing, and the sales
industry. It is not only to the algorithmic advances but also to data
availability, representing transactions, users, and background product
information. Finding products related in different ways, i.e., substitutes and
complements is essential for users' recommendations at the vendor's site and
for the vendor - to perform efficient assortment optimization.
  The paper introduces a novel method for finding products' substitutes and
complements based on the graph embedding Cleora algorithm. We also provide its
experimental evaluation with regards to the state-of-the-art Shopper algorithm,
studying the relevance of recommendations with surveys from industry experts.
It is concluded that the new approach presented here offers suitable choices of
recommended products, requiring a minimal amount of additional information. The
algorithm can be used in various enterprises, effectively identifying
substitute and complementary product options.
