  The primary goal in recommendation is to suggest relevant content to users,
but optimizing for accuracy often results in recommendations that lack
diversity. To remedy this, conventional approaches such as re-ranking improve
diversity by presenting more diverse items. Here we argue that to promote
inherent and prolonged diversity, the system must encourage its creation.
Towards this, we harness the performative nature of recommendation, and show
how learning can incentivize strategic content creators to create diverse
content. Our approach relies on a novel form of regularization that anticipates
strategic changes to content, and penalizes for content homogeneity. We provide
analytic and empirical results that demonstrate when and how diversity can be
incentivized, and experimentally demonstrate the utility of our approach on
synthetic and semi-synthetic data.
