Optimizing search advertising strategies: integrating reinforcement learning with generalized second-price auctions for enhanced ad ranking and bidding

This paper explores the integration of strategic optimization methods in the context of search advertising, focusing on ad ranking and bidding mechanisms within e-commerce platforms. Employing a combination of reinforcement learning and evolutionary strategies, we propose a dynamic model that adjusts to varying user interactions and optimizes the balance between advertiser cost, user relevance, and platform revenue. Our results suggest significant improvements in ad placement accuracy and cost-efficiency, demonstrating the model’s applicability in real-world scenarios.