论文标题
识别使用Cleora嵌入的替代品和互补产品进行优化
Identifying Substitute and Complementary Products for Assortment Optimization with Cleora Embeddings
论文作者
论文摘要
近年来，对机器学习算法在电子商务，全渠道营销和销售行业中的应用引起了人们的兴趣。它不仅符合算法的进步，而且要代表数据可用性，代表交易，用户和背景产品信息。以不同方式查找相关的产品，即替代品和补充对于供应商网站和供应商的建议至关重要，以进行有效的分类优化。 该论文介绍了一种基于嵌入Cleora算法的图形的替代品和补充的新方法。我们还对最先进的购物者算法进行了实验评估，研究了建议与行业专家的调查的相关性。结论是，此处介绍的新方法提供了适当的推荐产品选择，需要最少的其他信息。该算法可用于各种企业，有效地识别替代品和互补产品选项。
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.
