A latent-class model for estimating product-choice probabilities from clickstream data

作者：Nishimura Naoki; Sukegawa Noriyoshi; Takano Yuichi; Iwanaga Jiro

来源：Information Sciences, 2018, 429: 406-420.

DOI：10.1016/j.ins.2017.11.014

• Product choice
• Latent class
• EM algorithm
• Optimization
• E-commerce
• Clickstream data

This paper analyzes customer product-choice behavior based on the recency and frequency of each customer's page views on e-commerce sites. Recently, we devised an optimization model for estimating product-choice probabilities that satisfy monotonicity, convexity, and concavity constraints with respect to recency and frequency. This shape-restricted model delivered high predictive performance even when there were few training samples. However, typical e-commerce sites deal in many different varieties of product, so the predictive performance of the model can be further improved by integrating such product heterogeneity. For this purpose, we develop a novel latent-class shape-restricted model for estimating product-choice probabilities for each latent class of products. We also give a tailored expectation-maximization algorithm for parameter estimation. Computational results demonstrate that higher predictive performance is achieved with our latent-class model than with the previous shape-restricted model or latent-class logistic regression.

• 出版日期2018-3