Strategic Recommendation: Revenue Optimal Matching for Online Platforms (Student Abstract)
Luca D'Amico-Wong, Gary Qiurui Ma, David Parkes
Abstract
We consider a platform in a two-sided market with unit-supply sellers and unit-demand buyers. Each buyer can transact with a subset of sellers it knows off platform and another seller that the platform recommends. Given the choice of sellers, transactions and prices form a competitive equilibrium. The platform selects one seller for each buyer, and charges a fixed percentage of prices to all transactions that it recommends. The platform seeks to maximize total revenue. We show that the platform's problem is NP-hard, even when each buyer knows at most two buyers off platform. Finally, when each buyer values all sellers equally and knows only one buyer off platform, we provide a polynomial time algorithm that optimally solves the problem.
BibTeX
@article{D’Amico-Wong_Ma_Parkes_2024, title={Strategic Recommendation: Revenue Optimal Matching for Online Platforms (Student Abstract)}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30432}, DOI={10.1609/aaai.v38i21.30432}, abstractNote={We consider a platform in a two-sided market with unit-supply sellers and unit-demand buyers. Each buyer can transact with a subset of sellers it knows off platform and another seller that the platform recommends. Given the choice of sellers, transactions and prices form a competitive equilibrium. The platform selects one seller for each buyer, and charges a fixed percentage of prices to all transactions that it recommends. The platform seeks to maximize total revenue. We show that the platform’s problem is NP-hard, even when each buyer knows at most two buyers off platform. Finally, when each buyer values all sellers equally and knows only one buyer off platform, we provide a polynomial time algorithm that optimally solves the problem.}, number={21}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={D’Amico-Wong, Luca and Ma, Gary Qiurui and Parkes, David}, year={2024}, month={Mar.}, pages={23468-23470} }