Identifying Substitute and Complementary Products for Assortment Optimization with Cleora Embeddings
This work addresses assortment optimization for e-commerce vendors, but it appears incremental as it builds on existing embedding techniques.
The paper tackled the problem of identifying substitute and complementary products for assortment optimization in e-commerce by introducing a method based on the Cleora graph embedding algorithm, and it concluded that this approach offers suitable recommendations with minimal additional information.
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.