On online marketplaces, customers can access hundreds of reviews for a single product. Buyers often use reviews from other customers that share their 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 is joint work with Wenshuo Guo, Nika Haghtalab, and Kirthevasan Kandasamy.
Bio:
Ellen Vitercik is an Assistant Professor at Stanford University with a joint appointment between the Management Science & Engineering department and the Computer Science department. Her research revolves around machine learning theory, discrete optimization, and the interface between economics and computation. Before joining Stanford, she spent a year as a Miller Fellow at UC Berkeley after receiving a PhD in Computer Science from Carnegie Mellon University. Her thesis won the SIGecom Doctoral Dissertation Award and the CMU School of Computer Science Distinguished Dissertation Award.