Abstract:

In many electronic platforms, consumers leave reviews about their experiences. The body of reviews grows over time, providing a valuable source of information. Subsequent consumers as well as the platforms can make use of the information to learn about the quality of the products. The platforms, in particular, can use the information to better rank products, in order to meet consumer needs or to improve sales. We study how a platform can learn from consumer choices when they are presented with different product rankings, as well as from the reviews that they leave. Over time, the platform forms increasingly accurate estimates about product quality and consumer preferences. We provide an algorithm to simultaneously learn and provide personalized product rankings to consumers in order to maximize a cumulative expected reward. We use the notion of regret to quantify our algorithm’s performance. 

Bio:

Jingtong Zhao is a PhD candidate at the IEOR Department of Columbia University. She is working with Professor Van-Anh Truong, and is entering her fourth year.  Her current research is focused on designing and analyzing optimization methods for solving decision problems in information-rich and highly dynamic environments. Specifically, she works on the optimization of service systems such as e-commerce platforms and online medical care platforms, because the transition from a manufacturing economy to a service economy brings many new opportunities and challenges. Other areas of interest include healthcare admission and control policies, and data-driven supply-chain management. Jingtong received her B.S. in Financial Engineering from Columbia University in 2016. 

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