[1I3-GS-2] Machine learning: Market analysis

[1I3-GS-2-04] An Analytical Model of the Customer Purchase Factor Based on Conditional VAE Learned of Web Browsing Data

〇Tatsuya Kawakami¹, Yuta Sakai¹, Haruka Yamashita², Masayuki Goto¹ (1. Waseda University, 2. Sophia University)

Keywords:Conditional Variational Autoencoder, browsing history, purchasing factor analysis

Due to the accumulation of browsing history data on EC sites, Web marketing techniques are of growing significance. Most previous studies analyzed differences in overall browsing pages between purchasing and non-purchasing sessions by constructing a discriminative model and proposed measures for all users. However, it is difficult to utilize this model when considering personalized measures for each user. In this situation, a generative model, which infers browsing-behavior conditioned by whether a user purchases or not, is effective. Conditional VAE infers the data from the label and features of input data. In this paper, we apply Conditional VAE to browsing history data and identify important pages by generating a pseudo session assuming that a user in a non-purchasing session purchases. We propose a method to analyze important browsing pages that contribute to each user's purchase. We clarify the effectiveness of our proposed method by using real browsing history data.