Adaptive Ad Load Design for Sponsored Search Markets

Ad-load design is a central supply-side decision in sponsored search: more sponsored slots can raise revenue, but may crowd out organic results and degrade user outcomes. We formulate ad-load design as a nonstationary sequential decision problem in which the platform chooses query-level sponsored inventory while balancing revenue and total search conversions. We design exploration-augmented Locally Adaptive Ad Load (e-LAAL), an architecture that combines LAAL, a model-free query-level decision rule, with static user-level exploration arms that maintain support and provide fixed-policy counterfactual benchmarks. We provide a finite-time dynamic-regret guarantee for the e-LAAL architecture. In a platform-level production deployment serving 22.3 million users and 77.6 million searches, e-LAAL improves the empirical revenue-conversion tradeoff, achieving revenue comparable to high-ad-load static policies while preserving conversions close to low-ad-load benchmarks.