Performative Recommendation: Diversifying Content via Strategic Incentives
This addresses the issue of content homogeneity in recommendation systems for users and platforms, offering a novel approach rather than incremental improvements.
The paper tackles the problem of lacking diversity in recommendations by proposing a method to incentivize content creators to produce diverse content, showing through analytic and empirical results that diversity can be effectively encouraged.
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.