Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Session Chair: Jing Dong Session Chair: Rouba Ibrahim
Location:Forum 6
Presentations
Cold start to improve market thickness on online advertising platforms: data-driven algorithms and field experiments
Zikun Ye1, Dennis Zhang2, Heng Zhang3, Renyu Zhang4, Xin Chen1
1University of Illinois at Urbana Champaign, United States of America; 2Washington University in St. Louis; 3Arizona State University; 4New York University Shanghai
Discussant: Santiago Gallino (The Wharton School)
To solve the cold start problem on advertising platforms, we build a data-driven optimization model that captures the essential trade-off between short-term revenue and long-term market thickness on the platform, and propose a bandit algorithm to solve the problem. We also demonstrate the effectiveness of our algorithm via a novel two-sided randomized field experiment, and show our algorithm increases the cold start success rate by 62% and boosts the platform’s overall market thickness by 3.1%.
Synthetically Controlled Bandits
Vivek Farias3, Ciamac Moallemi2, Tianyi Peng4, Andrew Zheng1
1Operations Research Center, Massachusetts Institute of Technology, United States of America; 2Graduate School of Business, Columbia University, United States of America; 3Sloan School of Management, Massachusetts Institute of Technology, United States of America; 4Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, United States of America
Discussant: Hamsa Bastani (Wharton School, University of Pennsylvania)
We present a dynamic experimental design for settings where the experimental units are coarse (e.g. to mitigate interference). `Region-split' experiments on online platforms are one such setting. Our design, dubbed Synthetically Controlled Thompson Sampling (SCTS), minimizes the cost (i.e. regret) associated with experimentation at no meaningful loss to inferential ability. We provide theoretical guarantees and experiments highlighting the merits of SCTS relative to fixed and switchback designs.
