  Recommender systems present relevant contents to users and help content
creators reach their target audience. The dual nature of these systems
influences both users and creators: users' preferences are affected by the
items they are recommended, while creators are incentivized to alter their
contents 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 empirically, approaches for mitigating polarization and
promoting diversity in recommender systems. 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.
