This paper discusses a high-dimensional choice model for online retailers to address the challenge of understanding substitution patterns among a large number of products, which is difficult with traditional models. By leveraging consumer clickstream data and combining econometric and machine learning methods, specifically the graphical lasso technique, the authors aim to learn flexible substitution patterns based on product utility shocks. They demonstrate through synthetic data and a real-world empirical setting that their method provides more accurate demand forecasts and significantly improves the precision of estimating price elasticities, crucial inputs for operational decisions like inventory management, assortment planning, and pricing strategies. The research also highlights the value of integrating machine learning with econometrics and utilizing clickstream data in retail operations.