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

• 成果类型: Article
• 署名作者: Aouad, Ali; Feldman, Jacob; Segev, Danny; Zhang, Dennis J.
• 署名单位: Massachusetts Institute of Technology (MIT); Washington University (WUSTL); Tel Aviv University; Tel Aviv University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2021.00281
• 发表日期: 2025
• 关键词: MULTINOMIAL LOGIT MODEL; consideration sets; clickstream data; Approximation algorithms

摘要

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 clickbased 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.