Online Learning for Constrained Assortment Optimization under Markov Chain Choice Model

Li, Shukai; Luo, Qi; Huang, Zhiyuan; Shi, Cong

doi:10.2139/ssrn.4079753

Cited by 1 publication

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In practice, θ ˚is often unknown and needs to be estimated. Assuming no historical data of customers, dynamic assortment optimization adaptively learns θ ˚in a trial-and-error fashion by updating the assortment and observing the subsequent choices of customers sequentially (Caro & Gallien, 2007;Chen et al, 2020;Rusmevichientong et al, 2020;Chen et al, 2021b;Li et al, 2022). Meanwhile, in our era of Big Data, companies often collect abundant customer data.…”

Section: Introductionmentioning

confidence: 99%

PASTA: Pessimistic Assortment Optimization

Dong¹,

Mo²,

Qi³

et al. 2023

Preprint

View full text Add to dashboard Cite

We consider a class of assortment optimization problems in an offline data-driven setting. A firm does not know the underlying customer choice model but has access to an offline dataset consisting of the historically offered assortment set, customer choice, and revenue. The objective is to use the offline dataset to find an optimal assortment. Due to the combinatorial nature of assortment optimization, the problem of insufficient data coverage is likely to occur in the offline dataset. Therefore, designing a provably efficient offline learning algorithm becomes a significant challenge. To this end, we propose an algorithm referred to as Pessimistic ASsortment opTimizAtion (PASTA for short) designed based on the principle of pessimism, that can correctly identify the optimal assortment by only requiring the offline data to cover the optimal assortment under general settings. In particular, we establish a regret bound for the offline assortment optimization problem under the celebrated multinomial logit model. We also propose an efficient computational procedure to solve our pessimistic assortment optimization problem. Numerical studies demonstrate the superiority of the proposed method over the existing baseline method.

show abstract