A Latent-class Model for Estimating Product-choice Probabilities from Clickstream Data

TL;DR

This paper introduces a latent-class shape-restricted model that improves the prediction of product-choice probabilities from clickstream data by accounting for product heterogeneity, using an EM algorithm for parameter estimation.

Contribution

The paper develops a novel latent-class shape-restricted model and an EM algorithm, enhancing predictive accuracy over previous models and logistic regression.

Findings

Latent-class model outperforms previous shape-restricted model.

Higher predictive accuracy achieved with the latent-class approach.

Model effectively captures product heterogeneity.

Abstract

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.