Online Matching Frameworks Under Stochastic Rewards, Product Ranking, and Unknown Patience
针对电商中顾客滚动耐心未知、产品供应有限且多顾客陆续到达的复杂问题，提出一个解耦框架，将单顾客产品排序与多顾客在线匹配分开研究，并改进了级联点击模型下的排序算法。
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.