On online marketplaces, customers have access to hundreds of reviews for a single product. Buyers often use reviews from other customers that share their personal attributes—such as height for clothing or skin type for skincare products—to estimate their values, which they may not know a priori. Customers with few relevant reviews may hesitate to buy a product except at a low price, so for the seller, there is a tension between setting high prices and ensuring that there are enough reviews that buyers can confidently estimate their values. In this talk, we formulate this pricing problem through the lens of online learning and provide a no-regret learning algorithm.
This talk describes joint research with Wenshuo Guo, Nika Haghtalab, and Kirthevasan Kandasamy.
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