Assortment planning decisions of online marketplaces can have a significant impact on customers' choices. This incentivizes marketplaces to employ algorithms that prioritize marketplaces' short-term goals by featuring products with the highest popularity. However, these algorithms can lead to...

In many online platforms, customers' decisions are substantially influenced by product rankings as most customers only examine a few top-ranked products. Concurrently, such platforms also use the same data corresponding to customers' actions to learn how these products must be ranked or ordered....

We consider a setting in which potential buyers of an indivisible item have private and heterogeneous valuation and cost of capital. The valuation and cost of capital can be correlated with each other, but they are independent across buyers. In such a setting, a buyer's utility for the item is...

We study the problem of actively learning a non-parametric choice model based on consumers' decisions. We present a negative result showing that such choice models may not be identifiable. To overcome the identifiability problem, we introduce a directed acyclic graph (DAG) representation of the...

In online advertising markets, budget-constrained advertisers acquire ad placements through repeated bidding in auctions on various platforms. We present a strategy for bidding optimally in a set of auctions that may or may not be incentive-compatible under the presence of budget constraints....

Online platforms employ recommendation systems to enhance customer engagement and drive revenue. However, in a multi-sided platform where the platform interacts with diverse stakeholders such as sellers (items) and customers (users), each with their own desired outcomes, finding an appropriate...