The Click-Based MNL Model: A Framework for Modeling Click Data in Assortment Optimization

Ali Aouad (), Jacob Feldman (), Danny Segev () and Dennis J. Zhang ()
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Ali Aouad: Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142
Jacob Feldman: Olin Business School, Washington University, St. Louis, Missouri 63130
Danny Segev: School of Mathematical Sciences and Coller School of Management, Tel Aviv University, Tel Aviv 69978, Israel
Dennis J. Zhang: Olin Business School, Washington University, St. Louis, Missouri 63130

Management Science, 2025, vol. 71, issue 8, 6943-6960

Abstract: We introduce the click-based MNL choice model, a framework for capturing customer purchasing decisions in e-commerce settings. Specifically, we augment the classical Multinomial Logit choice model by assuming that customers only consider the items they have clicked on before they proceed to compare their random utilities. In this context, we study the resulting assortment optimization problem, where the objective is to select a subset of products, made available for purchase, to maximize the expected revenue. Our main algorithmic contribution comes in the form of a polynomial-time approximation scheme (PTAS) for this problem, showing that the optimal expected revenue can be efficiently approached within any degree of accuracy. To establish this result, we develop several technical ideas, including enumeration schemes and stochastic inequalities, which may be of broader interest. Using data from Alibaba’s online marketplace, we fit click-based MNL and latent class MNL models to historical sales and click data in a setting where the online platform recommends a personalized six-product display to each user. We propose an estimation methodology for the click-based MNL model that leverages clickstream data and machine learning classification algorithms. Our numerical results suggest that clickstream data are valuable for predicting choices and that the click-based MNL model can outperform standard logit-based models in certain settings.

Keywords: Multinomial Logit model; consideration sets; clickstream data; approximation algorithms (search for similar items in EconPapers)
Date: 2025
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