Controlling Creator Exposure under Recommendation Feedback

Recommendation platforms allocate visibility among creators and shape users’ content menus, while engagement feeds back into subsequent allocations. Controlling information oligopoly and information cocoons requires coordinating global exposure concentration and per-user variety while accounting for the randomness of serving. In this paper, we formulate the Constrained Welfare Occupancy Program (CWOP), a request-level allocation model that maximizes expected utility under concentration ceilings and per-user entropy floors. Our analysis separates the utility cost of these targets from optimization error and establishes why feasible plans can still violate targets during finite-window serving. We derive policy-dependent safety margins that provide simultaneous exposure guarantees across adaptive windows under a known request distribution and conditionally independent sampling, and bound the utility cost of certification. An anonymized real-platform audit finds that exposure concentration is approximately eight times audience-stock concentration on a matched creator subset, measured by the Herfindahl–Hirschman index, while the estimated elasticity of audience growth with respect to audience stock is approximately 0.7. These observational findings motivate direct exposure control, while our framework makes its feasibility, reliability, and utility tradeoffs explicit.