Click-based MNL: Algorithmic Frameworks for Modeling Click Data in Assortment Optimization
Ali Aouad
London Business School
Wednesday, Nov 20, 2019
4:30 - 5:30 PM
Location: Shriran 262
Abstract:
In this paper, we introduce the click-based MNL choice model, a novel framework for modeling customer purchasing decisions in e-commerce settings. Our main modeling idea is to assume that the click behavior within product recommendation or search results pages provides an exact signal regarding the alternatives considered by each customer. 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. In the course of establishing this result, we develop novel technical ideas, including enumeration schemes and stochastic inequalities, which may be of broader interest for related stochastic knapsack problems. Experiments on data acquired in collaboration with the retailer Alibaba demonstrate the practical significance of the proposed choice model. We generate realistic assortment optimization instances that mirror Alibaba's display customization problem, and implement practical variants of our approximation scheme to compute assortment recommendations in these settings. We find that the recommended assortments have the potential to be at least 9% more profitable than those resulting from a standard MNL model.
Joint work with Jacob Feldman, Danny Segev, and Dennis Zhang.
Operations Research Colloquia: http://or.stanford.edu/oras_seminars.html