Online Matching Frameworks Under Stochastic Rewards, Product Ranking, and Unknown Patience

Abstract

Ranking Products for Customers with Unknown Patience In e-commerce, customers have an unknown patience in terms of how far down the page they are willing to scroll. In light of this, how should products be ranked? The e-commerce retailer’s problem is further complicated by the fact that the supply of each product may be limited, and that multiple customers who are interested in these products will arrive over time. In “Online Matching Frameworks Under Stochastic Rewards, Product Ranking, and Unknown Patience,” Brubach, Grammel, Ma, and Srinivasan provide a general framework for studying this complicated problem that decouples the product ranking problem for a single customer from the online matching of products to multiple customers over time. They also develop a better algorithm for the single-customer product ranking problem under well-studied cascade-click models. Finally, they introduce a model where the products are also arriving over time and cannot be included in the search rankings until they arrive.