Learning to Optimize Decisions in Online Service Platforms
By: Jingtong Zhao
Publisher: [publisher not identified]
Published: 2021
Place of publication: [New York, N.Y.?]
Language: eng
Description: In this thesis, we consider how service platforms can provide personalized service to incoming consumers in order to improve ease and convenience for their users, or to enhance their own revenues. With an increasing trend toward digitalization, there is now a massive amount of data that can be leveraged to accomplish this goal. In this thesis, we explore how to leverage feature data about the consumers and products, as well as the way consumers interact with the platforms, in order to make better operational decisions, such as pricing, ranking, and recommendations of products and services. In Chapters 1 & 2, we study platforms in which consumers' purchasing decisions are strongly influenced by reviews that are posted by previous consumers. In Chapter 1, we consider how a platform can learn from the choices that consumers make, as well as the reviews they leave, in order to form increasingly accurate estimates about product quality and consumer preferences, so that the platform can provide better personalized product rankings.

In Chapter 2, we focus on platforms where each consumer forms an impression of a product by browsing the available reviews in a chosen order following a cascade click model. We consider how to rank the reviews as well as how to price a product given the reviews, in order to maximize short- and long-term revenue. In Chapter 3, we study a platform that offers a menu of memberships of different durations to a pool of heterogeneous consumers. A consumer's choice depends on both the menu prices and his personal intended usage time. The platform wants to maximize the long-term revenue while considering the choices made by heterogeneous customers. We show insights into the optimal solution of each problem when all the parameters are known. For the case where some parameters are initially unknown, we propose a joint learning and optimization algorithm, and provide theoretical guarantees for its performance.
Notes: Department: Industrial Engineering and Operations Research.

Thesis advisor: Van-Anh Truong.

Thesis (Ph.D.)--Columbia University, 2021.