Prof. Omar El Housni
Operations Research and Information Engineering
Cornell University
Talk:
Two-sided Assortment Optimization
Abstract:
Two-sided matching platforms have become increasingly prevalent due to their applications in labor markets, dating, accommodation, and ridesharing. A central challenge faced by these platforms is choice congestion, i.e., popular options often receive more requests than they can handle, leading to suboptimal market outcomes. To address this challenge, we introduce a two-sided assortment optimization framework that incorporates general choice preferences and constraints on the assortments shown to agents. The objective is to maximize the expected number of matches by determining both the assortments presented to agents and the order in which they are displayed. Within this framework, we define several policy classes that capture different levels of adaptivity in platform design. We characterize the performance gaps between all pairs of policy classes under general choice models and a variety of constraint types. These results demonstrate the trade-offs between adaptivity and performance, showing that a broad family of policies achieve outcomes within a constant factor of the most adaptive optimal policy. Furthermore, for each policy class, we develop algorithms that provide constant-factor approximations in both unconstrained and constrained settings. This is a joint work with Ulysse Hennebelle and Alfredo Torrico.
Biography:
Professor Omar El Housni is Assistant Professor in the School of Operations Research and Information Engineering and Cornell Tech at Cornell University. His research revolves around decision-making under uncertainty where he aims to design robust and efficient algorithms for a wide range of dynamic optimization problems with applications in revenue management and matching platforms. Professor Housni spent a few internships as a research and data scientist at Amazon and Uber where he contributed to the design and implementation of data-driven optimization models for matching and retailing platforms. Professor Housni holds a bachelor’s degree in applied mathematics from École Polytechnique (France), and a Master of Science and a Ph.D. in operations research from Columbia University.
