Motivated by online decision-making in time-varying combinatorial environments, we study
the problem of transforming offline algorithms to their online counterparts. We focus on …

In many online platforms, customers' decisions are substantially influenced by product
rankings as most customers only examine a few top-ranked products. Concurrently, such …

We consider the product-ranking challenge that online retailers face when their customers
typically behave as “window shoppers.” They form an impression of the assortment after …

The design of online policies for stochastic matching and revenue management settings is
usually bound by the Bayesian prior that the demand process is formed by a fixed-length …

Abstract Machine learning algorithms play an important role in a variety of important
decision-making processes, including targeted advertisement displays, home loan …

Online retailing has seen steady growth over the last decade. According to the Digital
Commerce (formerly Internet Retailer) analysis of the US Commerce Department's year-end …

Product ranking is the core problem for revenue-maximizing online retailers. To design
proper product ranking algorithms, various consumer choice models are proposed to …

A growing body of research suggests that an abundance of choices can lead to decision-
making difficulties for consumers. Rather than maximizing utility, many consumers employ a …

Rankings on online platforms help their end-users find the relevant information—people,
news, media, and products—quickly. Fair ranking tasks, which ask to rank a set of items to …

In this paper, we consider an assortment optimization problem in which a platform must
choose pairwise disjoint sets of assortments to offer across a series of T stages. Arriving …