A Latent-class Model for Estimating Product-choice Probabilities from Clickstream Data
This incremental improvement addresses the challenge of predicting customer choices more accurately for e-commerce sites dealing with diverse products.
The paper tackles the problem of estimating product-choice probabilities from e-commerce clickstream data by developing a latent-class shape-restricted model to account for product heterogeneity, achieving higher predictive performance than previous models and latent-class logistic regression.
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 products, so the predictive performance of the model can be further improved by integration of 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 and common latent-class logistic regression.