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

arXiv: https://arxiv.org/abs/1612.06589 · HTML: https://mlsanity.com/paper/1612.06589/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2016.

## Built on by (most cited first, 3 of 3)

- [OPP-Miner: Order-preserving sequential pattern mining](https://mlsanity.com/paper/2202.03140/) (2022)
- [Interpretable Price Bounds Estimation with Shape Constraints in Price Optimization](https://mlsanity.com/paper/2405.14909/) (2024)
- [BBE-LSWCM: A Bootstrapped Ensemble of Long and Short Window Clickstream Models](https://mlsanity.com/paper/2203.16155/) (2022)

## Abstract (arXiv)

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.