Abstract: Data describing how multiple users interact with an online system is gathered. Macro-level data is extracted from the collected data that is associated with overall trends of products offered by the online system. A machine learning model is trained using the macro-level data to receive features of a product as input to generate a supply-demand curve of the product. The machine learning model is applied to multiple products to generate a supply-demand curve for each product. Placements of at least a subset of products are determined based on the supply-demand curves of the products. A user's interaction with at least one product is predicted, upon the placements of the subset of products being presented to the user. The adjusted placements of the subset of products are presented to the user on a graphical user interface.

Abstract: Data describing how multiple users interact with an online system is gathered. For each user, a digital twin model is trained using reinforcement learning on their data to predict their interactions in various virtual environment variants. This model is then run in several candidate virtual environment variants to simulate how the user might interact with each. A score is assigned to each environment based on the likelihood of the user interacting in a specific way. The virtual environment variant with the best score is selected and displayed to the user.