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Users derive value from a recommender system (RS) only to the extent that itis able to surface content (or items) that meet their needs/preferences. WhileRSs often have a comprehensive view of user preferences across the entire userbase, content providers, by contrast, generally have only a local view of thepreferences of users that have interacted with their content. This limits aprovider's ability to offer new content to best serve the broader population.In this work, we tackle this information asymmetry with content promptingpolicies. A content prompt is a hint or suggestion to a provider to makeavailable novel content for which the RS predicts unmet user demand. Aprompting policy is a sequence of such prompts that is responsive to thedynamics of a provider's beliefs, skills and incentives. We aim to determine ajoint prompting policy that induces a set of providers to make contentavailable that optimizes user social welfare in equilibrium, while respectingthe incentives of the providers themselves. Our contributions include: (i) anabstract model of the RS ecosystem, including content provider behaviors, thatsupports such prompting; (ii) the design and theoretical analysis of sequentialprompting policies for individual providers; (iii) a mixed integer programmingformulation for optimal joint prompting using path planning in content space;and (iv) simple, proof-of-concept experiments illustrating how such policiesimprove ecosystem health and user welfare.

Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems