Abstract: Recommender systems are designed to serve the dual purpose of presenting relevant content to users, while also helping content creators reach their target audience. The dual nature of these systems naturally influences both users and creators: a user's preference can be altered by the items they are recommended, while creators may be incentivized to alter their content such that it is recommended more frequently.We define a model, called user-creator feature dynamics, to capture the dual influences of recommender systems.We prove that a recommender system with dual influence is guaranteed to polarize, causing diversity loss in the system.We then investigate, both theoretically and experimentally, approaches for promoting diversity in recommender systems as a means of mitigating polarization.Unexpectedly, we find that common diversity-promoting approaches do not work in the presence of dual influence, while relevancy-optimizing methods like top-$k$ recommendation can prevent polarization and improve diversity of the system.