[2B1-GS-2] Machine learning: Text mining

[2B1-GS-2-03] A Customer Analysis Method Considering the Diversity of Purchasing Behavior Based on the Knowledge Graph Attention Network

〇Daiki Fujiwara¹, Takuya Morikawa¹, Ayako Yamagiwa¹, Masayuki Goto¹ (1. Waseda University)

Keywords:E-commerce site, Customer analysis, Diversity of purchasing behavior, Knowledge Graph Attention Network, Clustering

In recent years, e-commerce sites have been used to conduct customer analysis, with the aim of building good relationships with customers and improving long-term sales. Evaluating the diversity of customers' purchasing behavior is particularly important as a marketing angle. The scalar values assigned to each customer in studies analyzing purchasing behavior diversity do not consider the varying impact of individual purchase items on the indices. Therefore, customers who should be treated with different business measures may be treated as the same. This study proposes a method for customer analysis that considers the impact of each purchase item on the index. The method calculates features that represent the diversity of each customer's purchase behavior by utilizing the distribution of weights assigned to each purchase in the Knowledge Graph Attention Network, a type of recommendation model. The effectiveness of the proposed method is demonstrated by applying it to real data.