Policy Design for Two-sided Platforms with Participation Dynamics
This addresses the challenge of optimizing platform health for stakeholders like viewers and providers, though it is incremental as it builds on existing control and game theory concepts.
This paper tackles the problem of recommendation policies in two-sided platforms, such as video streaming or e-commerce, by studying participation dynamics for the first time, and demonstrates that a simple algorithm improves long-term social welfare through experiments on synthetic and real data.
In two-sided platforms (e.g., video streaming or e-commerce), viewers and providers engage in interactive dynamics: viewers benefit from increases in provider populations, while providers benefit from increases in viewer population. Despite the importance of such "population effects" on long-term platform health, recommendation policies do not generally take the participation dynamics into account. This paper thus studies the dynamics and recommender policy design on two-sided platforms under the population effects for the first time. Our control- and game-theoretic findings warn against the use of the standard "myopic-greedy" policy and shed light on the importance of provider-side considerations (i.e., effectively distributing exposure among provider groups) to improve social welfare via population growth. We also present a simple algorithm to optimize long-term social welfare by taking the population effects into account, and demonstrate its effectiveness in synthetic and real-data experiments. Our experiment code is available at https://github.com/sdean-group/dynamics-two-sided-market.