We study a dynamic assortment selection problem where arriving customers make purchase decisions among offered products from a universe of $N$ products under a Markov-chain-based choice (MCBC) model. The retailer observes only the assortment and the customer's single choice per period. Given...

We study a personalized pricing problem with demand censoring in an offline data-driven setting. In this problem, a firm is endowed with a finite amount of inventory, and faces a random demand that is dependent on the offered price and the covariates (from products, customers, or both). Any...

We develop a new approach that integrates empirical estimation and assortment optimization to achieve display personalization for e-commerce platforms. We propose a two-stage Multinomial Logit (MNL) based consider-then-choose model, which accurately captures the two stages of a consumer's...

We consider the dynamic pricing problem of a monopolist seller who sells a set of mutually substitutable products over a finite time horizon. Customer demand is sensitive to the price of each individual product and the reference price which is formed from a comparison among the prices of all...

With the proliferation of mobile data services (MDS), increasing attention has been paid to factors affecting the adoption of mobile phones with access to MDS. In this paper, a theoretical framework based on means-end chain theory is proposed to examine users’ decision-making processes...