Online Advertising

Online advertising research covers auctions and real-time bidding, budget pacing, sponsored-search retrieval, ad ranking, and conversion prediction. Common approaches include online learning and control, causal experiments and attribution, reinforcement learning, and increasingly generative models for recommendations, creatives, and ads in AI-generated responses. Researchers evaluate these methods with auction benchmarks, platform logs, randomized experiments, and live A/B tests, while also studying privacy, disclosure, and effects on users. A central tension is improving revenue and campaign performance without sacrificing relevance, user welfare, reliable measurement, or safe deployment under changing market conditions.