Optimizing Search Advertising Strategies: Integrating Reinforcement Learning with Generalized Second-Price Auctions for Enhanced Ad Ranking and Bidding

2024/05/22 by Chang Zhou, Zhou, Chang, Yang Zhao +9 · 1 citation
Business, Management and Accounting · Decision Sciences · #Auction Theory and Applications #Consumer Market Behavior and Pricing #FOS: Computer and information sciences #Machine Learning (cs.LG) #Supply Chain and Inventory Management

paper · pdf · doi:10.48550/arxiv.2405.13381

openalex publication_date 2024/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper explores the integration of strategic optimization methods in search advertising, focusing on ad ranking and bidding mechanisms within E-commerce platforms. By 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.

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