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

Performative Recommendation: Diversifying Content via Strategic Incentives