【公开学术报告】Assortment Optimization for the Multinomial Logit Model with Repeated Customer Interactions

Assortment Optimization for the Multinomial Logit Model with Repeated Customer Interactions

Speaker: Chenhao Wang (CUHK-Shenzhen)

Date & Time: Tue. 22^nd, April 2025, from 10:00 AM to 11:30 AM (Beijing Time)

Zoom Meeting ID:  89292122048 (Password: 842598)

Join via the Link:   https://us02web.zoom.us/j/89292122048

ABSTRACT

This paper presents the multinomial logit model with repeated customer interactions. In each period, the same customer selects a product from the assortment recommended in that period or opts out. From the seller’s perspective, the choice probability is updated based on the purchase history. We study the adaptive assortment recommendation strategy for all the periods. Although the problem is generally intractable, as we show, when the customer interacts with the seller for two periods, we discover the structures of the optimal assortment when the available products in the two periods are identical and develop approximation algorithms in other cases. For more than two periods, we find that the optimal fixed assortments that are not adapted to the purchase history can achieve 68.47% or 50% of the optimal expected revenue, respectively, when the available products across periods are disjoint or not. Using two public datasets, we demonstrate that the model with repeated customer interactions can better predict the purchase behavior and generate higher revenues.

Keywords: repeated customer interactions, multinomial logit, sequential recommendation, assortment optimization, discrete choice models