Identifying Substitute and Complementary Products for Assortment Optimization with Cleora Embeddings

TL;DR

This paper presents a novel graph embedding method using Cleora to identify substitute and complementary products, enhancing assortment optimization in e-commerce with minimal additional data.

Contribution

Introduces a new approach using Cleora embeddings for product relation detection, outperforming existing algorithms like Shopper in recommendation relevance.

Findings

Effective identification of substitutes and complements

Requires minimal additional information

Suitable for various enterprise applications

Abstract

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.