Papers

Note: Author ordering on all papers is alphabetical, as is convention in OR/MS.

Accepted Papers

• Loot Box Pricing and Design with Ningyuan Chen, Adam N. Elmachtoub, and Xiao Lei. [Direct Link] [Slides]
  • Management Science (Dec. 2020)
  • Accepted in The 21st ACM Conference on Economics and Computation (EC), 2020.
  • Invited to present at the Federal Trade Commission (FTC) Workshop on Consumer Issues Related to Loot Boxes, 2019 (one of four research papers selected).
  • 1st Place, IBM Best Student Paper Award in Service Science, 2019 (to Xiao Lei).
• The Value of Personalized Pricing with Adam N. Elmachtoub and Vishal Gupta. [Direct Link] [Slides]
  • Management Science (April 2021)
  • Accepted in The 15th Conference on Web and Internet Economics (WINE), 2019.
  • Finalist, INFORMS Service Science Cluster Best Paper Award, 2018.
• The Power of Opaque Products in Pricing with Adam N. Elmachtoub. [Direct Link] [Slides]
  • Management Science (Jan. 2021)
  • Accepted in The 13th Conference on Web and Internet Economics (WINE), 2017.

Working Papers

• Feature-Based Market Segmentation and Pricing. [Slides]
  • Abstract

    With the rapid development of data-driven analytics, many firms have begun experimenting with personalized pricing strategies, i.e. strategies that predict a customer's valuation then offer them a tailored price. Ideally, a firm would perfectly predict each customer's valuation and price their goods accordingly. Unfortunately, in practice these valuations must be predicted by the firm using noisy regression models, and the number of prices the firm can offer is constrained by operational considerations. In this work, we give a general framework for analyzing and optimizing semi-personalized pricing strategies where the seller uses features about their customers to jointly segment and price their market. Specifically, we show how a seller can leverage a noisy valuation model to construct segmentation and pricing decisions with provable bounds on the lost revenue. We then give a series of the results that explain how a seller can improve their strategies by decomposing their lost profits as stemming from either prediction error or limited price flexibility. Along the way we prove a number of structural properties about monopoly pricing when valuations are the output of a regression model that may be of independent interest.
• Churning while Learning: Maximizing User Engagement in a Recommendation System w/ Raghav Singal. [Direct Link] [Slides]
  • Abstract

    Online media platforms such as Spotify, YouTube Music, and NetEase Cloud Music rely on long-term user engagement for revenue generation. The primary operational level under their control is content recommendation (i.e., what content to recommend to various users), where the right recommendation can induce users to further interact with the platform, sign-up for premium memberships, view ads, and more. However, given constantly updating supply (i.e., new content being created) and heterogeneous user behavior, optimal recommendation is challenging. It requires a careful balance between exploration (understanding the efficacy of new content) and exploitation (recommending well understood existing content). Motivated by a real-world impressions level dataset from NetEase Cloud Music, we propose a two-period model for the platform recommendation problem, with the aim of maximizing long term user engagement. Our model captures two key features of this marketplace: (1) supply-side learning (i.e., platform learning the efficacy of new content) and (2) demand-side heterogeneous churning (i.e., different users churning at different rates as a function of both their engagement and their types). We use our two-period model to understand the interplay between churning while learning and how it impacts the long-term user engagement. In addition to characterizing the structure of the optimal learning policy for the platform recommendation problem (i.e., which users to experiment on), we numerically illustrate our key findings on a wide range of parameters. We find that accounting for heterogeneous churning behavior while learning the efficacy of new content can lead to improvements of up to 14% in the long-term user engagement.
  • Finalist, 2020 RMP Data-Driven Research Challenge.
• Project 412Connect: Bridging Students to Communities w/ Alex DiChristofano, Sera Linardi, and Mara F. McCloud. [Direct Link]
  • Abstract

    In this work, we describe some of the challenges Black-owned businesses face both in the United States and specifically in the city of Pittsburgh. Taking into account local dynamics and the communicated desires of Black-owned businesses in the Pittsburgh region, we determine that university students represent an under-utilized market for these businesses. We investigate the root causes for this inefficiency and design and implement a platform, 412Connect (\url{https://www.412connect.org/}), to increase online support for Pittsburgh Black-owned businesses from students in the greater university community. The site operates by coordinating interactions between student users and participating businesses via targeted recommendations. For platform designers, we describe the project from its conception, paying special attention to our motivation and design choices. Our design choices are aided by two simple, novel models for badge design and recommendation systems that may be of theoretical interest. We also highlight challenges and lessons from coordinating a grassroots volunteer project working in conjunction with community partners, and the opportunities and pitfalls of engaged scholarship.